uid int64 4 318k | paper_url stringlengths 39 81 | arxiv_id stringlengths 9 16 ⌀ | title stringlengths 6 365 | abstract stringlengths 0 7.27k | url_abs stringlengths 17 601 | url_pdf stringlengths 21 819 | proceeding stringlengths 7 1.03k ⌀ | authors list | tasks list | date float64 422B 1,672B ⌀ | methods list | __index_level_0__ int64 1 197k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
130,304 | https://paperswithcode.com/paper/sparse-black-box-video-attack-with | 2001.03754 | Sparse Black-box Video Attack with Reinforcement Learning | Adversarial attacks on video recognition models have been explored recently. However, most existing works treat each video frame equally and ignore their temporal interactions. To overcome this drawback, a few methods try to select some key frames and then perform attacks based on them. Unfortunately, their selection s... | https://arxiv.org/abs/2001.03754v3 | https://arxiv.org/pdf/2001.03754v3.pdf | null | [
"Xingxing Wei",
"Huanqian Yan",
"Bo Li"
] | [
"reinforcement-learning",
"Video Recognition"
] | 1,578,700,800,000 | [] | 149,923 |
222,876 | https://paperswithcode.com/paper/meta-learning-for-downstream-aware-and | 2106.03270 | Meta-learning for downstream aware and agnostic pretraining | Neural network pretraining is gaining attention due to its outstanding performance in natural language processing applications. However, pretraining usually leverages predefined task sequences to learn general linguistic clues. The lack of mechanisms in choosing proper tasks during pretraining makes the learning and kn... | https://arxiv.org/abs/2106.03270v1 | https://arxiv.org/pdf/2106.03270v1.pdf | null | [
"Hongyin Luo",
"Shuyan Dong",
"Yung-Sung Chuang",
"Shang-Wen Li"
] | [
"Meta-Learning"
] | 1,622,937,600,000 | [] | 168,561 |
10,565 | https://paperswithcode.com/paper/learning-local-metrics-and-influential | 1802.03452 | Learning Local Metrics and Influential Regions for Classification | The performance of distance-based classifiers heavily depends on the
underlying distance metric, so it is valuable to learn a suitable metric from
the data. To address the problem of multimodality, it is desirable to learn
local metrics. In this short paper, we define a new intuitive distance with
local metrics and inf... | http://arxiv.org/abs/1802.03452v1 | http://arxiv.org/pdf/1802.03452v1.pdf | null | [
"Mingzhi Dong",
"Yujiang Wang",
"Xiaochen Yang",
"Jing-Hao Xue"
] | [
"Classification",
"Classification",
"Metric Learning"
] | 1,518,134,400,000 | [] | 13,944 |
226,653 | https://paperswithcode.com/paper/on-minimizing-cost-in-legal-document-review | 2106.09866 | On Minimizing Cost in Legal Document Review Workflows | Technology-assisted review (TAR) refers to human-in-the-loop machine learning workflows for document review in legal discovery and other high recall review tasks. Attorneys and legal technologists have debated whether review should be a single iterative process (one-phase TAR workflows) or whether model training and re... | https://arxiv.org/abs/2106.09866v1 | https://arxiv.org/pdf/2106.09866v1.pdf | null | [
"Eugene Yang",
"David D. Lewis",
"Ophir Frieder"
] | [
"Active Learning"
] | 1,623,974,400,000 | [] | 145,674 |
275,759 | https://paperswithcode.com/paper/sigma-a-structural-inconsistency-reducing | 2202.02797 | SIGMA: A Structural Inconsistency Reducing Graph Matching Algorithm | Graph matching finds the correspondence of nodes across two correlated graphs and lies at the core of many applications. When graph side information is not available, the node correspondence is estimated on the sole basis of network topologies. In this paper, we propose a novel criterion to measure the graph matching a... | https://arxiv.org/abs/2202.02797v1 | https://arxiv.org/pdf/2202.02797v1.pdf | null | [
"Weijie Liu",
"Chao Zhang",
"Nenggan Zheng",
"Hui Qian"
] | [
"Graph Matching"
] | 1,644,105,600,000 | [
{
"code_snippet_url": null,
"description": "Diffusion models generate samples by gradually\r\nremoving noise from a signal, and their training objective can be expressed as a reweighted variational lower-bound (https://arxiv.org/abs/2006.11239).",
"full_name": "Diffusion",
"introduced_year": 2000,
... | 171,920 |
69,079 | https://paperswithcode.com/paper/quantifying-training-challenges-of-dependency | null | Quantifying training challenges of dependency parsers | Not all dependencies are equal when training a dependency parser: some are straightforward enough to be learned with only a sample of data, others embed more complexity. This work introduces a series of metrics to quantify those differences, and thereby to expose the shortcomings of various parsing algorithms and strat... | https://aclanthology.org/C18-1270 | https://aclanthology.org/C18-1270.pdf | COLING 2018 8 | [
"Lauriane Aufrant",
"Guillaume Wisniewski",
"Fran{\\c{c}}ois Yvon"
] | [
"Cross-Lingual Transfer",
"Dependency Parsing"
] | 1,533,081,600,000 | [] | 102,018 |
145,877 | https://paperswithcode.com/paper/distributional-semantics-for-neo-latin | null | Distributional Semantics for Neo-Latin | We address the problem of creating and evaluating quality Neo-Latin word embeddings for the purpose of philosophical research, adapting the Nonce2Vec tool to learn embeddings from Neo-Latin sentences. This distributional semantic modeling tool can learn from tiny data incrementally, using a larger background corpus for... | https://aclanthology.org/2020.lt4hala-1.13 | https://aclanthology.org/2020.lt4hala-1.13.pdf | LREC 2020 5 | [
"Jelke Bloem",
"Maria Chiara Parisi",
"Martin Reynaert",
"Yvette Oortwijn",
"Arianna Betti"
] | [
"Word Embeddings"
] | 1,588,291,200,000 | [] | 134,617 |
144,930 | https://paperswithcode.com/paper/a-new-validity-index-for-fuzzy-possibilistic | 2005.09162 | A New Validity Index for Fuzzy-Possibilistic C-Means Clustering | In some complicated datasets, due to the presence of noisy data points and outliers, cluster validity indices can give conflicting results in determining the optimal number of clusters. This paper presents a new validity index for fuzzy-possibilistic c-means clustering called Fuzzy-Possibilistic (FP) index, which works... | https://arxiv.org/abs/2005.09162v1 | https://arxiv.org/pdf/2005.09162v1.pdf | null | [
"Mohammad Hossein Fazel Zarandi",
"Shahabeddin Sotudian",
"Oscar Castillo"
] | [
"Image Segmentation",
"Medical Image Segmentation",
"Semantic Segmentation"
] | 1,589,846,400,000 | [] | 173,006 |
111,802 | https://paperswithcode.com/paper/text-data-augmentation-made-simple-by | 1812.04718 | Text Data Augmentation Made Simple By Leveraging NLP Cloud APIs | In practice, it is common to find oneself with far too little text data to train a deep neural network. This "Big Data Wall" represents a challenge for minority language communities on the Internet, organizations, laboratories and companies that compete the GAFAM (Google, Amazon, Facebook, Apple, Microsoft). While most... | https://arxiv.org/abs/1812.04718v1 | https://arxiv.org/pdf/1812.04718v1.pdf | null | [
"Claude Coulombe"
] | [
"Data Augmentation",
"Text Augmentation"
] | 1,543,968,000,000 | [
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/96aaa311c0251d24decb9dc5da4957b7c590af6f/torch/nn/modules/activation.py#L277",
"description": "**Sigmoid Activations** are a type of activation function for neural networks:\r\n\r\n$$f\\left(x\\right) = \\frac{1}{\\left(1+\\exp\\left(-x\\right)\... | 23,888 |
299,827 | https://paperswithcode.com/paper/spatial-temporal-adaptive-graph-convolution | 2206.03128 | Spatial-Temporal Adaptive Graph Convolution with Attention Network for Traffic Forecasting | Traffic forecasting is one canonical example of spatial-temporal learning task in Intelligent Traffic System. Existing approaches capture spatial dependency with a pre-determined matrix in graph convolution neural operators. However, the explicit graph structure losses some hidden representations of relationships among... | https://arxiv.org/abs/2206.03128v1 | https://arxiv.org/pdf/2206.03128v1.pdf | null | [
"Chen Weikang",
"Li Yawen",
"Xue Zhe",
"LI ANG",
"Wu Guobin"
] | [
"Graph Attention",
"Time Series"
] | 1,654,560,000,000 | [
{
"code_snippet_url": null,
"description": "A **convolution** is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the part of the input it is on, then summing the results into an output.\r\n\r\nIntuitively,... | 147,064 |
143,983 | https://paperswithcode.com/paper/ice-gan-identity-aware-and-capsule-enhanced | 2005.04370 | ICE-GAN: Identity-aware and Capsule-Enhanced GAN with Graph-based Reasoning for Micro-Expression Recognition and Synthesis | Micro-expressions are reflections of people's true feelings and motives, which attract an increasing number of researchers into the study of automatic facial micro-expression recognition. The short detection window, the subtle facial muscle movements, and the limited training samples make micro-expression recognition c... | https://arxiv.org/abs/2005.04370v2 | https://arxiv.org/pdf/2005.04370v2.pdf | null | [
"Jianhui Yu",
"Chaoyi Zhang",
"Yang song",
"Weidong Cai"
] | [
"Micro-Expression Recognition"
] | 1,588,982,400,000 | [] | 17,625 |
20,766 | https://paperswithcode.com/paper/straight-to-shapes-real-time-detection-of | 1611.07932 | Straight to Shapes: Real-time Detection of Encoded Shapes | Current object detection approaches predict bounding boxes, but these provide
little instance-specific information beyond location, scale and aspect ratio.
In this work, we propose to directly regress to objects' shapes in addition to
their bounding boxes and categories. It is crucial to find an appropriate shape
repre... | http://arxiv.org/abs/1611.07932v2 | http://arxiv.org/pdf/1611.07932v2.pdf | CVPR 2017 7 | [
"Saumya Jetley",
"Michael Sapienza",
"Stuart Golodetz",
"Philip H. S. Torr"
] | [
"Denoising",
"Object Detection",
"Object Detection"
] | 1,479,859,200,000 | [] | 123,804 |
123,474 | https://paperswithcode.com/paper/fixing-implicit-derivatives-trust-region | null | Fixing Implicit Derivatives: Trust-Region Based Learning of Continuous Energy Functions | We present a new technique for the learning of continuous energy functions that
we refer to as Wibergian Learning. One common approach to inverse problems
is to cast them as an energy minimisation problem, where the minimum cost
solution found is used as an estimator of hidden parameters. Our new approach
formally char... | http://papers.nips.cc/paper/8427-fixing-implicit-derivatives-trust-region-based-learning-of-continuous-energy-functions | http://papers.nips.cc/paper/8427-fixing-implicit-derivatives-trust-region-based-learning-of-continuous-energy-functions.pdf | NeurIPS 2019 12 | [
"Chris Russell",
"Matteo Toso",
"Neill Campbell"
] | [
"3D Reconstruction"
] | 1,575,158,400,000 | [] | 80,007 |
288,083 | https://paperswithcode.com/paper/segmentation-consistent-probabilistic-lesion | 2204.05276 | Segmentation-Consistent Probabilistic Lesion Counting | Lesion counts are important indicators of disease severity, patient prognosis, and treatment efficacy, yet counting as a task in medical imaging is often overlooked in favor of segmentation. This work introduces a novel continuously differentiable function that maps lesion segmentation predictions to lesion count proba... | https://arxiv.org/abs/2204.05276v2 | https://arxiv.org/pdf/2204.05276v2.pdf | null | [
"Julien Schroeter",
"Chelsea Myers-Colet",
"Douglas L Arnold",
"Tal Arbel"
] | [
"Lesion Segmentation",
"Multi-Task Learning"
] | 1,649,635,200,000 | [
{
"code_snippet_url": "https://github.com/UCSC-REAL/HOC",
"description": "",
"full_name": "High-Order Consensuses",
"introduced_year": 2000,
"main_collection": {
"area": "Reinforcement Learning",
"description": "",
"name": "Value Function Estimation",
"parent": null
}... | 54,129 |
303,367 | https://paperswithcode.com/paper/object-detection-and-tracking-with-autonomous | 2206.12941 | Object Detection and Tracking with Autonomous UAV | In this paper, a combat Unmanned Air Vehicle (UAV) is modeled in the simulation environment. The rotary wing UAV is successfully performed various tasks such as locking on the targets, tracking, and sharing the relevant data with surrounding vehicles. Different software technologies such as API communication, ground co... | https://arxiv.org/abs/2206.12941v1 | https://arxiv.org/pdf/2206.12941v1.pdf | null | [
"A. Huzeyfe Demir",
"Berke Yavas",
"Mehmet Yazici",
"Dogukan Aksu",
"M. Ali Aydin"
] | [
"Object Detection",
"Object Detection"
] | 1,656,201,600,000 | [] | 5,527 |
178,653 | https://paperswithcode.com/paper/itreepack-protein-complex-side-chain-packing | 1504.05467 | iTreePack: Protein Complex Side-Chain Packing by Dual Decomposition | Protein side-chain packing is a critical component in obtaining the 3D
coordinates of a structure and drug discovery. Single-domain protein side-chain
packing has been thoroughly studied. A major challenge in generalizing these
methods to protein complexes is that they, unlike monomers, often have very
large treewidth,... | http://arxiv.org/abs/1504.05467v1 | http://arxiv.org/pdf/1504.05467v1.pdf | null | [] | [
"Drug Discovery"
] | 1,429,574,400,000 | [] | 44,922 |
221,626 | https://paperswithcode.com/paper/deep-fair-discriminative-clustering | 2105.14146 | Deep Fair Discriminative Clustering | Deep clustering has the potential to learn a strong representation and hence better clustering performance compared to traditional clustering methods such as $k$-means and spectral clustering. However, this strong representation learning ability may make the clustering unfair by discovering surrogates for protected inf... | https://arxiv.org/abs/2105.14146v1 | https://arxiv.org/pdf/2105.14146v1.pdf | null | [
"Hongjing Zhang",
"Ian Davidson"
] | [
"Deep Clustering",
"Fairness",
"Representation Learning"
] | 1,622,160,000,000 | [] | 56,203 |
308,243 | https://paperswithcode.com/paper/fldetector-detecting-malicious-clients-in | 2207.09209 | FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clients | Federated learning (FL) is vulnerable to model poisoning attacks, in which malicious clients corrupt the global model via sending manipulated model updates to the server. Existing defenses mainly rely on Byzantine-robust FL methods, which aim to learn an accurate global model even if some clients are malicious. However... | https://arxiv.org/abs/2207.09209v3 | https://arxiv.org/pdf/2207.09209v3.pdf | null | [
"Zaixi Zhang",
"Xiaoyu Cao",
"Jinyuan Jia",
"Neil Zhenqiang Gong"
] | [
"Federated Learning",
"Model Poisoning"
] | 1,658,188,800,000 | [] | 109,629 |
300,587 | https://paperswithcode.com/paper/multi-faceted-graph-attention-network-for | 2206.05168 | Multi-faceted Graph Attention Network for Radar Target Recognition in Heterogeneous Radar Network | Radar target recognition (RTR), as a key technology of intelligent radar systems, has been well investigated. Accurate RTR at low signal-to-noise ratios (SNRs) still remains an open challenge. Most existing methods are based on a single radar or the homogeneous radar network, which do not fully exploit frequency-dimens... | https://arxiv.org/abs/2206.05168v1 | https://arxiv.org/pdf/2206.05168v1.pdf | null | [
"Han Meng",
"Yuexing Peng",
"Wei Xiang",
"Xu Pang",
"Wenbo Wang"
] | [
"Graph Attention"
] | 1,654,819,200,000 | [] | 108,981 |
10,855 | https://paperswithcode.com/paper/a-method-for-restoring-the-training-set | 1802.01435 | A Method for Restoring the Training Set Distribution in an Image Classifier | Convolutional Neural Networks are a well-known staple of modern image
classification. However, it can be difficult to assess the quality and
robustness of such models. Deep models are known to perform well on a given
training and estimation set, but can easily be fooled by data that is
specifically generated for the pu... | http://arxiv.org/abs/1802.01435v1 | http://arxiv.org/pdf/1802.01435v1.pdf | null | [
"Alexey Chaplygin",
"Joshua Chacksfield"
] | [
"Classification",
"Image Classification"
] | 1,517,788,800,000 | [] | 160,998 |
72,197 | https://paperswithcode.com/paper/sketch-based-linear-value-function | null | Sketch-Based Linear Value Function Approximation | Hashing is a common method to reduce large, potentially infinite feature vectors to a fixed-size table. In reinforcement learning, hashing is often used in conjunction with tile coding to represent states in continuous spaces. Hashing is also a promising approach to value function approximation in large discrete domain... | http://papers.nips.cc/paper/4540-sketch-based-linear-value-function-approximation | http://papers.nips.cc/paper/4540-sketch-based-linear-value-function-approximation.pdf | NeurIPS 2012 12 | [
"Marc Bellemare",
"Joel Veness",
"Michael Bowling"
] | [
"Atari Games",
"reinforcement-learning"
] | 1,354,320,000,000 | [] | 163,640 |
168,173 | https://paperswithcode.com/paper/dipair-fast-and-accurate-distillation-for | 2010.03099 | DiPair: Fast and Accurate Distillation for Trillion-Scale Text Matching and Pair Modeling | Pre-trained models like BERT (Devlin et al., 2018) have dominated NLP / IR applications such as single sentence classification, text pair classification, and question answering. However, deploying these models in real systems is highly non-trivial due to their exorbitant computational costs. A common remedy to this is ... | https://arxiv.org/abs/2010.03099v1 | https://arxiv.org/pdf/2010.03099v1.pdf | Findings of the Association for Computational Linguistics 2020 | [
"Jiecao Chen",
"Liu Yang",
"Karthik Raman",
"Michael Bendersky",
"Jung-Jung Yeh",
"Yun Zhou",
"Marc Najork",
"Danyang Cai",
"Ehsan Emadzadeh"
] | [
"Knowledge Distillation",
"Question Answering",
"Sentence Classification",
"Text Matching"
] | 1,602,028,800,000 | [
{
"code_snippet_url": null,
"description": "A very simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data and then to average their predictions. Unfortunately, making predictions using a whole ensemble of models is cumbersome and may... | 115,131 |
34,958 | https://paperswithcode.com/paper/detecting-engagement-in-egocentric-video | 1604.00906 | Detecting Engagement in Egocentric Video | In a wearable camera video, we see what the camera wearer sees. While this
makes it easy to know roughly what he chose to look at, it does not immediately
reveal when he was engaged with the environment. Specifically, at what moments
did his focus linger, as he paused to gather more information about something
he saw? ... | http://arxiv.org/abs/1604.00906v1 | http://arxiv.org/pdf/1604.00906v1.pdf | null | [
"Yu-Chuan Su",
"Kristen Grauman"
] | [
"Video Summarization"
] | 1,459,728,000,000 | [] | 95,999 |
16,944 | https://paperswithcode.com/paper/eden-evolutionary-deep-networks-for-efficient | 1709.09161 | EDEN: Evolutionary Deep Networks for Efficient Machine Learning | Deep neural networks continue to show improved performance with increasing
depth, an encouraging trend that implies an explosion in the possible
permutations of network architectures and hyperparameters for which there is
little intuitive guidance. To address this increasing complexity, we propose
Evolutionary DEep Net... | http://arxiv.org/abs/1709.09161v1 | http://arxiv.org/pdf/1709.09161v1.pdf | null | [
"Emmanuel Dufourq",
"Bruce A. Bassett"
] | [
"Sentiment Analysis"
] | 1,506,384,000,000 | [
{
"code_snippet_url": null,
"description": "**Max Pooling** is a pooling operation that calculates the maximum value for patches of a feature map, and uses it to create a downsampled (pooled) feature map. It is usually used after a convolutional layer. It adds a small amount of translation invariance - mea... | 61,919 |
219,256 | https://paperswithcode.com/paper/pay-attention-to-mlps | 2105.08050 | Pay Attention to MLPs | Transformers have become one of the most important architectural innovations in deep learning and have enabled many breakthroughs over the past few years. Here we propose a simple network architecture, gMLP, based on MLPs with gating, and show that it can perform as well as Transformers in key language and vision appli... | https://arxiv.org/abs/2105.08050v2 | https://arxiv.org/pdf/2105.08050v2.pdf | NeurIPS 2021 12 | [
"Hanxiao Liu",
"Zihang Dai",
"David R. So",
"Quoc V. Le"
] | [
"Image Classification",
"Natural Language Inference",
"Question Answering",
"Sentiment Analysis"
] | 1,621,209,600,000 | [
{
"code_snippet_url": "",
"description": "**Spatial Gating Unit**, or **SGU**, is a gating unit used in the [gMLP](https://paperswithcode.com/method/gmlp) architecture to captures spatial interactions. To enable cross-token interactions, it is necessary for the layer $s(\\cdot)$ to contain a contraction ope... | 69,185 |
248,425 | https://paperswithcode.com/paper/exact-and-bounded-collision-probability-for | 2110.06348 | Exact and Bounded Collision Probability for Motion Planning under Gaussian Uncertainty | Computing collision-free trajectories is of prime importance for safe navigation. We present an approach for computing the collision probability under Gaussian distributed motion and sensing uncertainty with the robot and static obstacle shapes approximated as ellipsoids. The collision condition is formulated as the di... | https://arxiv.org/abs/2110.06348v1 | https://arxiv.org/pdf/2110.06348v1.pdf | null | [
"Antony Thomas",
"Fulvio Mastrogiovanni",
"Marco Baglietto"
] | [
"Motion Planning"
] | 1,633,996,800,000 | [] | 84,651 |
267,257 | https://paperswithcode.com/paper/kge-cl-contrastive-learning-of-knowledge | 2112.04871 | KGE-CL: Contrastive Learning of Knowledge Graph Embeddings | Learning the embeddings of knowledge graphs is vital in artificial intelligence, and can benefit various downstream applications, such as recommendation and question answering. In recent years, many research efforts have been proposed for knowledge graph embedding. However, most previous knowledge graph embedding metho... | https://arxiv.org/abs/2112.04871v1 | https://arxiv.org/pdf/2112.04871v1.pdf | null | [
"Wentao Xu",
"Zhiping Luo",
"Weiqing Liu",
"Jiang Bian",
"Jian Yin",
"Tie-Yan Liu"
] | [
"Contrastive Learning",
"Graph Embedding",
"Knowledge Graph Embedding",
"Knowledge Graph Embeddings",
"Knowledge Graphs",
"Question Answering",
"Semantic Similarity",
"Semantic Textual Similarity"
] | 1,639,008,000,000 | [
{
"code_snippet_url": null,
"description": "",
"full_name": null,
"introduced_year": 2000,
"main_collection": {
"area": "Graphs",
"description": "",
"name": "Graph Representation Learning",
"parent": null
},
"name": "Contrastive Learning",
"source_title": null... | 100,793 |
223,005 | https://paperswithcode.com/paper/oriented-object-detection-with-transformer | 2106.03146 | Oriented Object Detection with Transformer | Object detection with Transformers (DETR) has achieved a competitive performance over traditional detectors, such as Faster R-CNN. However, the potential of DETR remains largely unexplored for the more challenging task of arbitrary-oriented object detection problem. We provide the first attempt and implement Oriented O... | https://arxiv.org/abs/2106.03146v1 | https://arxiv.org/pdf/2106.03146v1.pdf | null | [
"Teli Ma",
"Mingyuan Mao",
"Honghui Zheng",
"Peng Gao",
"Xiaodi Wang",
"Shumin Han",
"Errui Ding",
"Baochang Zhang",
"David Doermann"
] | [
"Object Detection",
"Object Detection"
] | 1,622,937,600,000 | [
{
"code_snippet_url": "https://github.com/facebookresearch/Detectron/blob/8170b25b425967f8f1c7d715bea3c5b8d9536cd8/detectron/modeling/FPN.py#L117",
"description": "A **Feature Pyramid Network**, or **FPN**, is a feature extractor that takes a single-scale image of an arbitrary size as input, and outputs pro... | 19,912 |
274,499 | https://paperswithcode.com/paper/tensor-recovery-based-on-tensor-equivalent | 2201.12709 | Low-Rank Tensor Completion Based on Bivariate Equivalent Minimax-Concave Penalty | Low-rank tensor completion (LRTC) is an important problem in computer vision and machine learning. The minimax-concave penalty (MCP) function as a non-convex relaxation has achieved good results in the LRTC problem. To makes all the constant parameters of the MCP function as variables so that futherly improving the ada... | https://arxiv.org/abs/2201.12709v3 | https://arxiv.org/pdf/2201.12709v3.pdf | null | [
"HongBing Zhang",
"Xinyi Liu",
"HongTao Fan",
"YaJing Li",
"Yinlin Ye"
] | [
"Denoising"
] | 1,643,500,800,000 | [] | 88,475 |
120,295 | https://paperswithcode.com/paper/direct-estimation-of-differential-functional | 1910.09701 | Direct Estimation of Differential Functional Graphical Models | We consider the problem of estimating the difference between two functional undirected graphical models with shared structures. In many applications, data are naturally regarded as high-dimensional random function vectors rather than multivariate scalars. For example, electroencephalography (EEG) data are more appropri... | https://arxiv.org/abs/1910.09701v2 | https://arxiv.org/pdf/1910.09701v2.pdf | NeurIPS 2019 12 | [
"Boxin Zhao",
"Y. Samuel Wang",
"Mladen Kolar"
] | [
"EEG"
] | 1,571,702,400,000 | [] | 151,189 |
151,751 | https://paperswithcode.com/paper/the-unimelb-submission-to-the-sigmorphon-2020 | null | The UniMelb Submission to the SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection | The paper describes the University of Melbourne{'}s submission to the SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection. Our team submitted three systems in total, two neural and one non-neural. Our analysis of systems{'} performance shows positive effects of newly introduced data hallucinat... | https://aclanthology.org/2020.sigmorphon-1.20 | https://aclanthology.org/2020.sigmorphon-1.20.pdf | WS 2020 7 | [
"Andreas Scherbakov"
] | [
"Morphological Inflection"
] | 1,593,561,600,000 | [] | 83,497 |
125,390 | https://paperswithcode.com/paper/controllable-list-wise-ranking-for-universal | 1911.10566 | Controllable List-wise Ranking for Universal No-reference Image Quality Assessment | No-reference image quality assessment (NR-IQA) has received increasing attention in the IQA community since reference image is not always available. Real-world images generally suffer from various types of distortion. Unfortunately, existing NR-IQA methods do not work with all types of distortion. It is a challenging t... | https://arxiv.org/abs/1911.10566v2 | https://arxiv.org/pdf/1911.10566v2.pdf | null | [
"Fu-Zhao Ou",
"Yuan-Gen Wang",
"Jin Li",
"Guopu Zhu",
"Sam Kwong"
] | [
"Image Quality Assessment",
"No-Reference Image Quality Assessment"
] | 1,574,553,600,000 | [] | 45,377 |
51,226 | https://paperswithcode.com/paper/cail2018-a-large-scale-legal-dataset-for | 1807.02478 | CAIL2018: A Large-Scale Legal Dataset for Judgment Prediction | In this paper, we introduce the \textbf{C}hinese \textbf{AI} and \textbf{L}aw
challenge dataset (CAIL2018), the first large-scale Chinese legal dataset for
judgment prediction. \dataset contains more than $2.6$ million criminal cases
published by the Supreme People's Court of China, which are several times
larger than ... | http://arxiv.org/abs/1807.02478v1 | http://arxiv.org/pdf/1807.02478v1.pdf | null | [
"Chaojun Xiao",
"Haoxi Zhong",
"Zhipeng Guo",
"Cunchao Tu",
"Zhiyuan Liu",
"Maosong Sun",
"Yansong Feng",
"Xianpei Han",
"Zhen Hu",
"Heng Wang",
"Jianfeng Xu"
] | [
"Text Classification",
"Text Classification"
] | 1,530,662,400,000 | [] | 131,560 |
133,791 | https://paperswithcode.com/paper/analytic-marching-an-analytic-meshing | 2002.06597 | Analytic Marching: An Analytic Meshing Solution from Deep Implicit Surface Networks | This paper studies a problem of learning surface mesh via implicit functions in an emerging field of deep learning surface reconstruction, where implicit functions are popularly implemented as multi-layer perceptrons (MLPs) with rectified linear units (ReLU). To achieve meshing from learned implicit functions, existing... | https://arxiv.org/abs/2002.06597v1 | https://arxiv.org/pdf/2002.06597v1.pdf | ICML 2020 1 | [
"Jiabao Lei",
"Kui Jia"
] | [
"Surface Reconstruction"
] | 1,581,811,200,000 | [
{
"code_snippet_url": "https://github.com/DimTrigkakis/Python-Net/blob/efb81b2f828da5a81b77a141245efdb0d5bcfbf8/incredibleMathFunctions.py#L12-L13",
"description": "**Rectified Linear Units**, or **ReLUs**, are a type of activation function that are linear in the positive dimension, but zero in the negative... | 168,938 |
706 | https://paperswithcode.com/paper/embedding-text-in-hyperbolic-spaces | 1806.04313 | Embedding Text in Hyperbolic Spaces | Natural language text exhibits hierarchical structure in a variety of
respects. Ideally, we could incorporate our prior knowledge of this
hierarchical structure into unsupervised learning algorithms that work on text
data. Recent work by Nickel & Kiela (2017) proposed using hyperbolic instead of
Euclidean embedding spa... | http://arxiv.org/abs/1806.04313v1 | http://arxiv.org/pdf/1806.04313v1.pdf | WS 2018 6 | [
"Bhuwan Dhingra",
"Christopher J. Shallue",
"Mohammad Norouzi",
"Andrew M. Dai",
"George E. Dahl"
] | [
"Sentence Embedding"
] | 1,528,761,600,000 | [] | 15,895 |
282,961 | https://paperswithcode.com/paper/coda-a-real-world-road-corner-case-dataset | 2203.07724 | CODA: A Real-World Road Corner Case Dataset for Object Detection in Autonomous Driving | Contemporary deep-learning object detection methods for autonomous driving usually assume prefixed categories of common traffic participants, such as pedestrians and cars. Most existing detectors are unable to detect uncommon objects and corner cases (e.g., a dog crossing a street), which may lead to severe accidents i... | https://arxiv.org/abs/2203.07724v3 | https://arxiv.org/pdf/2203.07724v3.pdf | null | [
"Kaican Li",
"Kai Chen",
"Haoyu Wang",
"Lanqing Hong",
"Chaoqiang Ye",
"Jianhua Han",
"Yukuai Chen",
"Wei zhang",
"Chunjing Xu",
"Dit-yan Yeung",
"Xiaodan Liang",
"Zhenguo Li",
"Hang Xu"
] | [
"Autonomous Driving",
"Object Detection",
"Object Detection"
] | 1,647,302,400,000 | [] | 139,101 |
125,295 | https://paperswithcode.com/paper/architectural-configurations-atlas | 1911.11024 | Architectural configurations, atlas granularity and functional connectivity with diagnostic value in Autism Spectrum Disorder | Currently, the diagnosis of Autism Spectrum Disorder (ASD) is dependent upon a subjective, time-consuming evaluation of behavioral tests by an expert clinician. Non-invasive functional MRI (fMRI) characterizes brain connectivity and may be used to inform diagnoses and democratize medicine. However, successful construct... | https://arxiv.org/abs/1911.11024v2 | https://arxiv.org/pdf/1911.11024v2.pdf | null | [
"Cooper J. Mellema",
"Alex Treacher",
"Kevin P. Nguyen",
"Albert Montillo"
] | [
"Feature Importance"
] | 1,574,640,000,000 | [] | 9,620 |
80,759 | https://paperswithcode.com/paper/privacy-preserving-off-policy-evaluation | 1902.00174 | Privacy Preserving Off-Policy Evaluation | Many reinforcement learning applications involve the use of data that is
sensitive, such as medical records of patients or financial information.
However, most current reinforcement learning methods can leak information
contained within the (possibly sensitive) data on which they are trained. To
address this problem, w... | http://arxiv.org/abs/1902.00174v1 | http://arxiv.org/pdf/1902.00174v1.pdf | null | [
"Tengyang Xie",
"Philip S. Thomas",
"Gerome Miklau"
] | [
"Privacy Preserving",
"reinforcement-learning"
] | 1,548,979,200,000 | [] | 181,357 |
14,000 | https://paperswithcode.com/paper/time-contrastive-learning-based-dnn | 1704.02373 | Time-Contrastive Learning Based DNN Bottleneck Features for Text-Dependent Speaker Verification | In this paper, we present a time-contrastive learning (TCL) based bottleneck (BN)feature extraction method for speech signals with an application to text-dependent (TD) speaker verification (SV). It is well-known that speech signals exhibit quasi-stationary behavior in and only in a short interval, and the TCL method a... | https://arxiv.org/abs/1704.02373v3 | https://arxiv.org/pdf/1704.02373v3.pdf | null | [
"Achintya Kr. Sarkar",
"Zheng-Hua Tan"
] | [
"Contrastive Learning",
"Speaker Verification",
"Text-Dependent Speaker Verification"
] | 1,491,436,800,000 | [] | 158,838 |
139,747 | https://paperswithcode.com/paper/speaker-change-aware-crf-for-dialogue-act | 2004.02913 | Speaker-change Aware CRF for Dialogue Act Classification | Recent work in Dialogue Act (DA) classification approaches the task as a sequence labeling problem, using neural network models coupled with a Conditional Random Field (CRF) as the last layer. CRF models the conditional probability of the target DA label sequence given the input utterance sequence. However, the task in... | https://arxiv.org/abs/2004.02913v2 | https://arxiv.org/pdf/2004.02913v2.pdf | COLING 2020 8 | [
"Guokan Shang",
"Antoine Jean-Pierre Tixier",
"Michalis Vazirgiannis",
"Jean-Pierre Lorré"
] | [
"Classification",
"Dialogue Act Classification",
"Classification"
] | 1,586,131,200,000 | [
{
"code_snippet_url": null,
"description": "**Conditional Random Fields** or **CRFs** are a type of probabilistic graph model that take neighboring sample context into account for tasks like classification. Prediction is modeled as a graphical model, which implements dependencies between the predictions. Gr... | 131,539 |
241,314 | https://paperswithcode.com/paper/parallel-constraint-driven-inductive-logic | 2109.07132 | Parallel Constraint-Driven Inductive Logic Programming | Multi-core machines are ubiquitous. However, most inductive logic programming (ILP) approaches use only a single core, which severely limits their scalability. To address this limitation, we introduce parallel techniques based on constraint-driven ILP where the goal is to accumulate constraints to restrict the hypothes... | https://arxiv.org/abs/2109.07132v1 | https://arxiv.org/pdf/2109.07132v1.pdf | null | [
"Andrew Cropper",
"Oghenejokpeme Orhobor",
"Cristian Dinu",
"Rolf Morel"
] | [
"Inductive logic programming",
"Program Synthesis"
] | 1,631,664,000,000 | [] | 32,199 |
751 | https://paperswithcode.com/paper/swarming-for-faster-convergence-in-stochastic | 1806.04207 | Swarming for Faster Convergence in Stochastic Optimization | We study a distributed framework for stochastic optimization which is
inspired by models of collective motion found in nature (e.g., swarming) with
mild communication requirements. Specifically, we analyze a scheme in which
each one of $N > 1$ independent threads, implements in a distributed and
unsynchronized fashion,... | http://arxiv.org/abs/1806.04207v2 | http://arxiv.org/pdf/1806.04207v2.pdf | null | [
"Shi Pu",
"Alfredo Garcia"
] | [
"Stochastic Optimization"
] | 1,528,675,200,000 | [] | 190,127 |
211,367 | https://paperswithcode.com/paper/pareto-efficient-fairness-in-supervised | 2104.01634 | Pareto Efficient Fairness in Supervised Learning: From Extraction to Tracing | As algorithmic decision-making systems are becoming more pervasive, it is crucial to ensure such systems do not become mechanisms of unfair discrimination on the basis of gender, race, ethnicity, religion, etc. Moreover, due to the inherent trade-off between fairness measures and accuracy, it is desirable to learn fair... | https://arxiv.org/abs/2104.01634v1 | https://arxiv.org/pdf/2104.01634v1.pdf | null | [
"Mohammad Mahdi Kamani",
"Rana Forsati",
"James Z. Wang",
"Mehrdad Mahdavi"
] | [
"Bilevel Optimization",
"Fairness"
] | 1,617,494,400,000 | [] | 123,351 |
243,015 | https://paperswithcode.com/paper/deep-structured-instance-graph-for-distilling | 2109.12862 | Deep Structured Instance Graph for Distilling Object Detectors | Effectively structuring deep knowledge plays a pivotal role in transfer from teacher to student, especially in semantic vision tasks. In this paper, we present a simple knowledge structure to exploit and encode information inside the detection system to facilitate detector knowledge distillation. Specifically, aiming a... | https://arxiv.org/abs/2109.12862v1 | https://arxiv.org/pdf/2109.12862v1.pdf | ICCV 2021 10 | [
"Yixin Chen",
"Pengguang Chen",
"Shu Liu",
"LiWei Wang",
"Jiaya Jia"
] | [
"Instance Segmentation",
"Knowledge Distillation",
"Object Detection",
"Object Detection",
"Semantic Segmentation"
] | 1,632,700,800,000 | [
{
"code_snippet_url": null,
"description": "The **Softmax** output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$ and a weighting vector $w$ we have:\r\n\r\n$$ P(y=j \\mid{x}) = \\frac{e^{x^{T}w_{j}}... | 99,950 |
171,552 | https://paperswithcode.com/paper/cog-connecting-new-skills-to-past-experience | 2010.14500 | COG: Connecting New Skills to Past Experience with Offline Reinforcement Learning | Reinforcement learning has been applied to a wide variety of robotics problems, but most of such applications involve collecting data from scratch for each new task. Since the amount of robot data we can collect for any single task is limited by time and cost considerations, the learned behavior is typically narrow: th... | https://arxiv.org/abs/2010.14500v1 | https://arxiv.org/pdf/2010.14500v1.pdf | null | [
"Avi Singh",
"Albert Yu",
"Jonathan Yang",
"Jesse Zhang",
"Aviral Kumar",
"Sergey Levine"
] | [
"reinforcement-learning"
] | 1,603,756,800,000 | [] | 71,736 |
206,694 | https://paperswithcode.com/paper/conformalized-survival-analysis | 2103.09763 | Conformalized Survival Analysis | Existing survival analysis techniques heavily rely on strong modelling assumptions and are, therefore, prone to model misspecification errors. In this paper, we develop an inferential method based on ideas from conformal prediction, which can wrap around any survival prediction algorithm to produce calibrated, covariat... | https://arxiv.org/abs/2103.09763v2 | https://arxiv.org/pdf/2103.09763v2.pdf | null | [
"Emmanuel J. Candès",
"Lihua Lei",
"Zhimei Ren"
] | [
"Survival Analysis",
"Survival Prediction"
] | 1,615,939,200,000 | [] | 147,259 |
142,725 | https://paperswithcode.com/paper/will-they-won-t-they-a-very-large-dataset-for | 2005.00388 | Will-They-Won't-They: A Very Large Dataset for Stance Detection on Twitter | We present a new challenging stance detection dataset, called Will-They-Won't-They (WT-WT), which contains 51,284 tweets in English, making it by far the largest available dataset of the type. All the annotations are carried out by experts; therefore, the dataset constitutes a high-quality and reliable benchmark for fu... | https://arxiv.org/abs/2005.00388v1 | https://arxiv.org/pdf/2005.00388v1.pdf | ACL 2020 6 | [
"Costanza Conforti",
"Jakob Berndt",
"Mohammad Taher Pilehvar",
"Chryssi Giannitsarou",
"Flavio Toxvaerd",
"Nigel Collier"
] | [
"Stance Detection"
] | 1,588,291,200,000 | [] | 41,408 |
262,857 | https://paperswithcode.com/paper/tracking-momentary-attention-fluctuations | null | Tracking momentary attention fluctuations with an EEG-based cognitive brain-machine interface | Momentary fluctuations in attention (perceptual accuracy) correlate with neural activity fluctuations in primate visual areas. Yet, the link between such momentary neural fluctuations and attention state remains to be shown in the human brain. We investigate this link using a real-time cognitive brain machine interface... | https://openreview.net/forum?id=ryeT47FIIS | https://openreview.net/pdf?id=ryeT47FIIS | null | [
"Anonymous"
] | [
"EEG"
] | 1,568,160,000,000 | [] | 55,501 |
21,974 | https://paperswithcode.com/paper/retrosynthetic-reaction-prediction-using | 1706.01643 | Retrosynthetic reaction prediction using neural sequence-to-sequence models | We describe a fully data driven model that learns to perform a retrosynthetic
reaction prediction task, which is treated as a sequence-to-sequence mapping
problem. The end-to-end trained model has an encoder-decoder architecture that
consists of two recurrent neural networks, which has previously shown great
success in... | http://arxiv.org/abs/1706.01643v1 | http://arxiv.org/pdf/1706.01643v1.pdf | null | [
"Bowen Liu",
"Bharath Ramsundar",
"Prasad Kawthekar",
"Jade Shi",
"Joseph Gomes",
"Quang Luu Nguyen",
"Stephen Ho",
"Jack Sloane",
"Paul Wender",
"Vijay Pande"
] | [
"Machine Translation"
] | 1,496,707,200,000 | [] | 192,631 |
4,923 | https://paperswithcode.com/paper/beyond-word-importance-contextual | 1801.05453 | Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs | The driving force behind the recent success of LSTMs has been their ability
to learn complex and non-linear relationships. Consequently, our inability to
describe these relationships has led to LSTMs being characterized as black
boxes. To this end, we introduce contextual decomposition (CD), an
interpretation algorithm... | http://arxiv.org/abs/1801.05453v2 | http://arxiv.org/pdf/1801.05453v2.pdf | ICLR 2018 1 | [
"W. James Murdoch",
"Peter J. Liu",
"Bin Yu"
] | [
"Sentiment Analysis"
] | 1,516,060,800,000 | [
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/96aaa311c0251d24decb9dc5da4957b7c590af6f/torch/nn/modules/activation.py#L277",
"description": "**Sigmoid Activations** are a type of activation function for neural networks:\r\n\r\n$$f\\left(x\\right) = \\frac{1}{\\left(1+\\exp\\left(-x\\right)\... | 83,597 |
132,010 | https://paperswithcode.com/paper/an-autonomous-intrusion-detection-system | 2001.11936 | An Autonomous Intrusion Detection System Using an Ensemble of Advanced Learners | An intrusion detection system (IDS) is a vital security component of modern computer networks. With the increasing amount of sensitive services that use computer network-based infrastructures, IDSs need to be more intelligent and autonomous. Aside from autonomy, another important feature for an IDS is its ability to de... | https://arxiv.org/abs/2001.11936v2 | https://arxiv.org/pdf/2001.11936v2.pdf | null | [
"Amir Andalib",
"Vahid Tabataba Vakili"
] | [
"Intrusion Detection"
] | 1,580,428,800,000 | [] | 69,801 |
40,755 | https://paperswithcode.com/paper/an-iterative-step-function-estimator-for | 1412.2129 | An iterative step-function estimator for graphons | Exchangeable graphs arise via a sampling procedure from measurable functions
known as graphons. A natural estimation problem is how well we can recover a
graphon given a single graph sampled from it. One general framework for
estimating a graphon uses step-functions obtained by partitioning the nodes of
the graph accor... | http://arxiv.org/abs/1412.2129v2 | http://arxiv.org/pdf/1412.2129v2.pdf | null | [
"Diana Cai",
"Nathanael Ackerman",
"Cameron Freer"
] | [
"Graphon Estimation"
] | 1,417,737,600,000 | [] | 176,724 |
106,279 | https://paperswithcode.com/paper/joint-reasoning-for-temporal-and-causal-1 | 1906.04941 | Joint Reasoning for Temporal and Causal Relations | Understanding temporal and causal relations between events is a fundamental natural language understanding task. Because a cause must be before its effect in time, temporal and causal relations are closely related and one relation even dictates the other one in many cases. However, limited attention has been paid to st... | https://arxiv.org/abs/1906.04941v1 | https://arxiv.org/pdf/1906.04941v1.pdf | ACL 2018 7 | [
"Qiang Ning",
"Zhili Feng",
"Hao Wu",
"Dan Roth"
] | [
"Natural Language Understanding"
] | 1,560,297,600,000 | [] | 133,095 |
214,527 | https://paperswithcode.com/paper/a-survey-of-active-learning-algorithms-for | 2104.07784 | A survey of active learning algorithms for supervised remote sensing image classification | Defining an efficient training set is one of the most delicate phases for the success of remote sensing image classification routines. The complexity of the problem, the limited temporal and financial resources, as well as the high intraclass variance can make an algorithm fail if it is trained with a suboptimal datase... | https://arxiv.org/abs/2104.07784v1 | https://arxiv.org/pdf/2104.07784v1.pdf | null | [
"Devis Tuia",
"Michele Volpi",
"Loris Copa",
"Mikhail Kanevski",
"Jordi Munoz-Mari"
] | [
"Active Learning",
"Classification",
"Hyperspectral Image Classification",
"Image Classification",
"Remote Sensing Image Classification"
] | 1,618,444,800,000 | [] | 9,895 |
199,404 | https://paperswithcode.com/paper/imagechd-a-3d-computed-tomography-image | 2101.10799 | ImageCHD: A 3D Computed Tomography Image Dataset for Classification of Congenital Heart Disease | Congenital heart disease (CHD) is the most common type of birth defect, which occurs 1 in every 110 births in the United States. CHD usually comes with severe variations in heart structure and great artery connections that can be classified into many types. Thus highly specialized domain knowledge and the time-consumin... | https://arxiv.org/abs/2101.10799v2 | https://arxiv.org/pdf/2101.10799v2.pdf | null | [
"Xiaowei Xu",
"Tianchen Wang",
"Jian Zhuang",
"Haiyun Yuan",
"Meiping Huang",
"Jianzheng Cen",
"Qianjun Jia",
"Yuhao Dong",
"Yiyu Shi"
] | [
"Classification",
"Computed Tomography (CT)",
"Classification"
] | 1,611,619,200,000 | [] | 119,238 |
76,886 | https://paperswithcode.com/paper/safe-scale-aware-feature-encoder-for-scene | 1901.05770 | SAFE: Scale Aware Feature Encoder for Scene Text Recognition | In this paper, we address the problem of having characters with different
scales in scene text recognition. We propose a novel scale aware feature
encoder (SAFE) that is designed specifically for encoding characters with
different scales. SAFE is composed of a multi-scale convolutional encoder and a
scale attention net... | http://arxiv.org/abs/1901.05770v1 | http://arxiv.org/pdf/1901.05770v1.pdf | null | [
"Wei Liu",
"Chaofeng Chen",
"Kwan-Yee K. Wong"
] | [
"Scene Text Recognition"
] | 1,547,683,200,000 | [] | 161,607 |
59,261 | https://paperswithcode.com/paper/feature-selection-via-sparse-approximation | 1102.02748 | Feature Selection via Sparse Approximation for Face Recognition | Inspired by biological vision systems, the over-complete local features with
huge cardinality are increasingly used for face recognition during the last
decades. Accordingly, feature selection has become more and more important and
plays a critical role for face data description and recognition. In this paper,
we propo... | http://arxiv.org/abs/1102.2748v1 | http://arxiv.org/pdf/1102.2748v1.pdf | null | [
"Yixiong Liang",
"Lei Wang",
"Yao Xiang",
"Beiji Zou"
] | [
"Face Recognition"
] | 1,297,641,600,000 | [] | 16,373 |
60,448 | https://paperswithcode.com/paper/polarity-loss-for-zero-shot-object-detection | 1811.08982 | Polarity Loss for Zero-shot Object Detection | Conventional object detection models require large amounts of training data. In comparison, humans can recognize previously unseen objects by merely knowing their semantic description. To mimic similar behaviour, zero-shot object detection aims to recognize and localize 'unseen' object instances by using only their sem... | https://arxiv.org/abs/1811.08982v3 | https://arxiv.org/pdf/1811.08982v3.pdf | null | [
"Shafin Rahman",
"Salman Khan",
"Nick Barnes"
] | [
"Metric Learning",
"Object Detection",
"Object Detection",
"Zero-Shot Learning",
"Zero-Shot Object Detection"
] | 1,542,844,800,000 | [] | 150,971 |
313,911 | https://paperswithcode.com/paper/rethinking-cost-sensitive-classification-in | 2208.11739 | Rethinking Cost-sensitive Classification in Deep Learning via Adversarial Data Augmentation | Cost-sensitive classification is critical in applications where misclassification errors widely vary in cost. However, over-parameterization poses fundamental challenges to the cost-sensitive modeling of deep neural networks (DNNs). The ability of a DNN to fully interpolate a training dataset can render a DNN, evaluate... | https://arxiv.org/abs/2208.11739v1 | https://arxiv.org/pdf/2208.11739v1.pdf | null | [
"Qiyuan Chen",
"Raed Al Kontar",
"Maher Nouiehed",
"Jessie Yang",
"Corey Lester"
] | [
"Data Augmentation"
] | 1,661,299,200,000 | [] | 52,548 |
152,344 | https://paperswithcode.com/paper/inductive-unsupervised-domain-adaptation-for | 2006.12816 | Inductive Unsupervised Domain Adaptation for Few-Shot Classification via Clustering | Few-shot classification tends to struggle when it needs to adapt to diverse domains. Due to the non-overlapping label space between domains, the performance of conventional domain adaptation is limited. Previous work tackles the problem in a transductive manner, by assuming access to the full set of test data, which is... | https://arxiv.org/abs/2006.12816v1 | https://arxiv.org/pdf/2006.12816v1.pdf | null | [
"Xin Cong",
"Bowen Yu",
"Tingwen Liu",
"Shiyao Cui",
"Hengzhu Tang",
"Bin Wang"
] | [
"Classification",
"Domain Adaptation",
"Classification",
"Unsupervised Domain Adaptation"
] | 1,592,870,400,000 | [
{
"code_snippet_url": null,
"description": "**Cosine Annealing** is a type of learning rate schedule that has the effect of starting with a large learning rate that is relatively rapidly decreased to a minimum value before being increased rapidly again. The resetting of the learning rate acts like a simulat... | 84,534 |
199,793 | https://paperswithcode.com/paper/neural-particle-image-velocimetry | 2101.11950 | Neural Particle Image Velocimetry | In the past decades, great progress has been made in the field of optical and particle-based measurement techniques for experimental analysis of fluid flows. Particle Image Velocimetry (PIV) technique is widely used to identify flow parameters from time-consecutive snapshots of particles injected into the fluid. The co... | https://arxiv.org/abs/2101.11950v1 | https://arxiv.org/pdf/2101.11950v1.pdf | null | [
"Nikolay Stulov",
"Michael Chertkov"
] | [
"Optical Flow Estimation"
] | 1,611,792,000,000 | [] | 136,461 |
162,032 | https://paperswithcode.com/paper/deep-generative-model-for-image-inpainting | 2009.01031 | Deep Generative Model for Image Inpainting with Local Binary Pattern Learning and Spatial Attention | Deep learning (DL) has demonstrated its powerful capabilities in the field of image inpainting. The DL-based image inpainting approaches can produce visually plausible results, but often generate various unpleasant artifacts, especially in the boundary and highly textured regions. To tackle this challenge, in this work... | https://arxiv.org/abs/2009.01031v1 | https://arxiv.org/pdf/2009.01031v1.pdf | null | [
"Haiwei Wu",
"Jiantao Zhou",
"Yuanman Li"
] | [
"Image Inpainting"
] | 1,599,004,800,000 | [
{
"code_snippet_url": "https://github.com/DimTrigkakis/Python-Net/blob/efb81b2f828da5a81b77a141245efdb0d5bcfbf8/incredibleMathFunctions.py#L12-L13",
"description": "**Rectified Linear Units**, or **ReLUs**, are a type of activation function that are linear in the positive dimension, but zero in the negative... | 112,962 |
130,040 | https://paperswithcode.com/paper/privacy-preserving-deep-learning-computation | 2001.02932 | Privacy-Preserving Deep Learning Computation for Geo-Distributed Medical Big-Data Platforms | This paper proposes a distributed deep learning framework for privacy-preserving medical data training. In order to avoid patients' data leakage in medical platforms, the hidden layers in the deep learning framework are separated and where the first layer is kept in platform and others layers are kept in a centralized ... | https://arxiv.org/abs/2001.02932v1 | https://arxiv.org/pdf/2001.02932v1.pdf | null | [
"Joohyung Jeon",
"Junhui Kim",
"Joongheon Kim",
"Kwangsoo Kim",
"Aziz Mohaisen",
"Jong-Kook Kim"
] | [
"Privacy Preserving",
"Privacy Preserving Deep Learning"
] | 1,578,528,000,000 | [] | 17,842 |
129,777 | https://paperswithcode.com/paper/general-partial-label-learning-via-dual | 2001.01290 | General Partial Label Learning via Dual Bipartite Graph Autoencoder | We formulate a practical yet challenging problem: General Partial Label Learning (GPLL). Compared to the traditional Partial Label Learning (PLL) problem, GPLL relaxes the supervision assumption from instance-level -- a label set partially labels an instance -- to group-level: 1) a label set partially labels a group of... | https://arxiv.org/abs/2001.01290v2 | https://arxiv.org/pdf/2001.01290v2.pdf | null | [
"Brian Chen",
"Bo Wu",
"Alireza Zareian",
"Hanwang Zhang",
"Shih-Fu Chang"
] | [
"Partial Label Learning"
] | 1,578,182,400,000 | [
{
"code_snippet_url": "https://github.com/L1aoXingyu/pytorch-beginner/blob/9c86be785c7c318a09cf29112dd1f1a58613239b/08-AutoEncoder/simple_autoencoder.py#L38",
"description": "An **Autoencoder** is a bottleneck architecture that turns a high-dimensional input into a latent low-dimensional code (encoder), and... | 3,686 |
288,463 | https://paperswithcode.com/paper/regression-or-classification-reflection-on-bp | 2204.05605 | Regression or Classification? Reflection on BP prediction from PPG data using Deep Neural Networks in the scope of practical applications | Photoplethysmographic (PPG) signals offer diagnostic potential beyond heart rate analysis or blood oxygen level monitoring. In the recent past, research focused extensively on non-invasive PPG-based approaches to blood pressure (BP) estimation. These approaches can be subdivided into regression and classification metho... | https://arxiv.org/abs/2204.05605v1 | https://arxiv.org/pdf/2204.05605v1.pdf | null | [
"Fabian Schrumpf",
"Paul Rudi Serdack",
"Mirco Fuchs"
] | [
"Classification"
] | 1,649,721,600,000 | [] | 50,890 |
260,009 | https://paperswithcode.com/paper/generalization-guarantee-of-sgd-for-pairwise | null | Generalization Guarantee of SGD for Pairwise Learning | Recently, there is a growing interest in studying pairwise learning since it includes many important machine learning tasks as specific examples, e.g., metric learning, AUC maximization and ranking. While stochastic gradient descent (SGD) is an efficient method, there is a lacking study on its generalization behavior f... | http://proceedings.neurips.cc/paper/2021/hash/b1301141feffabac455e1f90a7de2054-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/b1301141feffabac455e1f90a7de2054-Paper.pdf | NeurIPS 2021 12 | [
"Yunwen Lei",
"Mingrui Liu",
"Yiming Ying"
] | [
"Generalization Bounds",
"Metric Learning"
] | 1,638,316,800,000 | [
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/4e0ac120e9a8b096069c2f892488d630a5c8f358/torch/optim/sgd.py#L97-L112",
"description": "**Stochastic Gradient Descent** is an iterative optimization technique that uses minibatches of data to form an expectation of the gradient, rather than the f... | 51,822 |
183,037 | https://paperswithcode.com/paper/uav-enabled-mobile-edge-computing-offloading | 1802.03906 | UAV-Enabled Mobile Edge Computing: Offloading Optimization and Trajectory Design | With the emergence of diverse mobile applications (such as augmented
reality), the quality of experience of mobile users is greatly limited by their
computation capacity and finite battery lifetime. Mobile edge computing (MEC)
and wireless power transfer are promising to address this issue. However, these
two technique... | http://arxiv.org/abs/1802.03906v1 | http://arxiv.org/pdf/1802.03906v1.pdf | null | [] | [
"Edge-computing"
] | 1,518,393,600,000 | [] | 2,316 |
79,644 | https://paperswithcode.com/paper/sports-field-localization-via-deep-structured | null | Sports Field Localization via Deep Structured Models | In this work, we propose a novel way of efficiently localizing a sports field from a single broadcast image of the game. Related work in this area relies on manually annotating a few key frames and extending the localization to similar images, or installing fixed specialized cameras in the stadium from which the layout... | http://openaccess.thecvf.com/content_cvpr_2017/html/Homayounfar_Sports_Field_Localization_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Homayounfar_Sports_Field_Localization_CVPR_2017_paper.pdf | CVPR 2017 7 | [
"Namdar Homayounfar",
"Sanja Fidler",
"Raquel Urtasun"
] | [
"Semantic Segmentation"
] | 1,498,867,200,000 | [] | 179,728 |
27,743 | https://paperswithcode.com/paper/neural-emoji-recommendation-in-dialogue | 1612.04609 | Neural Emoji Recommendation in Dialogue Systems | Emoji is an essential component in dialogues which has been broadly utilized
on almost all social platforms. It could express more delicate feelings beyond
plain texts and thus smooth the communications between users, making dialogue
systems more anthropomorphic and vivid. In this paper, we focus on
automatically recom... | http://arxiv.org/abs/1612.04609v1 | http://arxiv.org/pdf/1612.04609v1.pdf | null | [
"Ruobing Xie",
"Zhiyuan Liu",
"Rui Yan",
"Maosong Sun"
] | [
"Classification"
] | 1,481,673,600,000 | [
{
"code_snippet_url": null,
"description": "The **Softmax** output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$ and a weighting vector $w$ we have:\r\n\r\n$$ P(y=j \\mid{x}) = \\frac{e^{x^{T}w_{j}}... | 134,485 |
144,043 | https://paperswithcode.com/paper/roteqnet-rotation-equivariant-network-for | 2005.04286 | RotEqNet: Rotation-Equivariant Network for Fluid Systems with Symmetric High-Order Tensors | In the recent application of scientific modeling, machine learning models are largely applied to facilitate computational simulations of fluid systems. Rotation symmetry is a general property for most symmetric fluid systems. However, in general, current machine learning methods have no theoretical way to guarantee rot... | https://arxiv.org/abs/2005.04286v1 | https://arxiv.org/pdf/2005.04286v1.pdf | null | [
"Liyao Gao",
"Yifan Du",
"Hongshan Li",
"Guang Lin"
] | [
"Data Augmentation"
] | 1,588,032,000,000 | [] | 42,288 |
62,049 | https://paperswithcode.com/paper/seq2graph-discovering-dynamic-dependencies | 1812.04448 | seq2graph: Discovering Dynamic Dependencies from Multivariate Time Series with Multi-level Attention | Discovering temporal lagged and inter-dependencies in multivariate time
series data is an important task. However, in many real-world applications,
such as commercial cloud management, manufacturing predictive maintenance, and
portfolios performance analysis, such dependencies can be non-linear and
time-variant, which ... | http://arxiv.org/abs/1812.04448v1 | http://arxiv.org/pdf/1812.04448v1.pdf | null | [
"Xuan-Hong Dang",
"Syed Yousaf Shah",
"Petros Zerfos"
] | [
"Time Series"
] | 1,544,140,800,000 | [] | 192,139 |
226,088 | https://paperswithcode.com/paper/c-3-compositional-counterfactual-constrastive | 2106.08914 | $C^3$: Compositional Counterfactual Constrastive Learning for Video-grounded Dialogues | Video-grounded dialogue systems aim to integrate video understanding and dialogue understanding to generate responses that are relevant to both the dialogue and video context. Most existing approaches employ deep learning models and have achieved remarkable performance, given the relatively small datasets available. Ho... | https://arxiv.org/abs/2106.08914v1 | https://arxiv.org/pdf/2106.08914v1.pdf | null | [
"Hung Le",
"Nancy F. Chen",
"Steven C. H. Hoi"
] | [
"Contrastive Learning",
"Dialogue Understanding",
"Video Understanding"
] | 1,623,801,600,000 | [
{
"code_snippet_url": null,
"description": "",
"full_name": null,
"introduced_year": 2000,
"main_collection": {
"area": "Graphs",
"description": "",
"name": "Graph Representation Learning",
"parent": null
},
"name": "Contrastive Learning",
"source_title": null... | 141,886 |
201,055 | https://paperswithcode.com/paper/switching-variational-auto-encoders-for-noise | 2102.04144 | Switching Variational Auto-Encoders for Noise-Agnostic Audio-visual Speech Enhancement | Recently, audio-visual speech enhancement has been tackled in the unsupervised settings based on variational auto-encoders (VAEs), where during training only clean data is used to train a generative model for speech, which at test time is combined with a noise model, e.g. nonnegative matrix factorization (NMF), whose p... | https://arxiv.org/abs/2102.04144v1 | https://arxiv.org/pdf/2102.04144v1.pdf | null | [
"Mostafa Sadeghi",
"Xavier Alameda-Pineda"
] | [
"Speech Enhancement"
] | 1,612,742,400,000 | [
{
"code_snippet_url": "https://github.com/AntixK/PyTorch-VAE/blob/8700d245a9735640dda458db4cf40708caf2e77f/models/vanilla_vae.py#L8",
"description": "A **Variational Autoencoder** is a type of likelihood-based generative model. It consists of an encoder, that takes in data $x$ as input and transforms this i... | 74,743 |
306,801 | https://paperswithcode.com/paper/poetictts-controllable-poetry-reading-for | 2207.05549 | PoeticTTS -- Controllable Poetry Reading for Literary Studies | Speech synthesis for poetry is challenging due to specific intonation patterns inherent to poetic speech. In this work, we propose an approach to synthesise poems with almost human like naturalness in order to enable literary scholars to systematically examine hypotheses on the interplay between text, spoken realisatio... | https://arxiv.org/abs/2207.05549v1 | https://arxiv.org/pdf/2207.05549v1.pdf | null | [
"Julia Koch",
"Florian Lux",
"Nadja Schauffler",
"Toni Bernhart",
"Felix Dieterle",
"Jonas Kuhn",
"Sandra Richter",
"Gabriel Viehhauser",
"Ngoc Thang Vu"
] | [
"Speech Synthesis"
] | 1,657,497,600,000 | [] | 63,743 |
75,398 | https://paperswithcode.com/paper/random-projection-in-deep-neural-networks | 1812.09489 | Random Projection in Deep Neural Networks | This work investigates the ways in which deep learning methods can benefit
from random projection (RP), a classic linear dimensionality reduction method.
We focus on two areas where, as we have found, employing RP techniques can
improve deep models: training neural networks on high-dimensional data and
initialization o... | http://arxiv.org/abs/1812.09489v1 | http://arxiv.org/pdf/1812.09489v1.pdf | null | [
"Piotr Iwo Wójcik"
] | [
"Dimensionality Reduction"
] | 1,545,436,800,000 | [] | 160,899 |
200,685 | https://paperswithcode.com/paper/dual-embedding-based-neural-collaborative | 2102.02549 | Dual-embedding based Neural Collaborative Filtering for Recommender Systems | Among various recommender techniques, collaborative filtering (CF) is the most successful one. And a key problem in CF is how to represent users and items. Previous works usually represent a user (an item) as a vector of latent factors (aka. \textit{embedding}) and then model the interactions between users and items ba... | https://arxiv.org/abs/2102.02549v2 | https://arxiv.org/pdf/2102.02549v2.pdf | null | [
"Gongshan He",
"Dongxing Zhao",
"Lixin Ding"
] | [
"Collaborative Filtering",
"Recommendation Systems"
] | 1,612,396,800,000 | [] | 121,820 |
72,786 | https://paperswithcode.com/paper/new-adaptive-algorithms-for-online | null | New Adaptive Algorithms for Online Classification | We propose a general framework to online learning for classification problems with time-varying potential functions in the adversarial setting. This framework allows to design and prove relative mistake bounds for any generic loss function. The mistake bounds can be specialized for the hinge loss, allowing to r... | http://papers.nips.cc/paper/4017-new-adaptive-algorithms-for-online-classification | http://papers.nips.cc/paper/4017-new-adaptive-algorithms-for-online-classification.pdf | NeurIPS 2010 12 | [
"Francesco Orabona",
"Koby Crammer"
] | [
"Classification",
"Classification",
"online learning"
] | 1,291,161,600,000 | [] | 168,550 |
317,863 | https://paperswithcode.com/paper/automated-ischemic-stroke-lesion-segmentation | 2209.09546 | Automated ischemic stroke lesion segmentation from 3D MRI | Ischemic Stroke Lesion Segmentation challenge (ISLES 2022) offers a platform for researchers to compare their solutions to 3D segmentation of ischemic stroke regions from 3D MRIs. In this work, we describe our solution to ISLES 2022 segmentation task. We re-sample all images to a common resolution, use two input MRI mo... | https://arxiv.org/abs/2209.09546v2 | https://arxiv.org/pdf/2209.09546v2.pdf | null | [
"Md Mahfuzur Rahman Siddique",
"Dong Yang",
"Yufan He",
"Daguang Xu",
"Andriy Myronenko"
] | [
"Ischemic Stroke Lesion Segmentation",
"Lesion Segmentation",
"Semantic Segmentation"
] | 1,663,632,000,000 | [] | 95,079 |
231,468 | https://paperswithcode.com/paper/continuous-variable-neural-network-quantum | 2107.07105 | Continuous-variable neural-network quantum states and the quantum rotor model | We initiate the study of neural-network quantum state algorithms for analyzing continuous-variable lattice quantum systems in first quantization. A simple family of continuous-variable trial wavefunctons is introduced which naturally generalizes the restricted Boltzmann machine (RBM) wavefunction introduced for analyzi... | https://arxiv.org/abs/2107.07105v1 | https://arxiv.org/pdf/2107.07105v1.pdf | null | [
"James Stokes",
"Saibal De",
"Shravan Veerapaneni",
"Giuseppe Carleo"
] | [
"Quantization",
"Variational Monte Carlo"
] | 1,626,307,200,000 | [
{
"code_snippet_url": null,
"description": "**Restricted Boltzmann Machines**, or **RBMs**, are two-layer generative neural networks that learn a probability distribution over the inputs. They are a special class of Boltzmann Machine in that they have a restricted number of connections between visible and h... | 141,496 |
90,646 | https://paperswithcode.com/paper/coalt-a-software-for-comparing-automatic | null | CoALT: A Software for Comparing Automatic Labelling Tools | Speech-text alignment tools are frequently used in speech technology and research. In this paper, we propose a GPL software CoALT (Comparing Automatic Labelling Tools) for comparing two automatic labellers or two speech-text alignment tools, ranking them and displaying statistics about their differences. The main featu... | https://aclanthology.org/L12-1042 | https://aclanthology.org/L12-1042.pdf | LREC 2012 5 | [
"Dominique Fohr",
"Odile Mella"
] | [
"Speech Recognition",
"Speech Synthesis"
] | 1,335,830,400,000 | [] | 61,523 |
99,072 | https://paperswithcode.com/paper/twittermancer-predicting-interactions-on | 1904.11119 | TwitterMancer: Predicting Interactions on Twitter Accurately | This paper investigates the interplay between different types of user
interactions on Twitter, with respect to predicting missing or unseen
interactions. For example, given a set of retweet interactions between Twitter
users, how accurately can we predict reply interactions? Is it more difficult
to predict retweet or q... | http://arxiv.org/abs/1904.11119v1 | http://arxiv.org/pdf/1904.11119v1.pdf | null | [
"Konstantinos Sotiropoulos",
"John W. Byers",
"Polyvios Pratikakis",
"Charalampos E. Tsourakakis"
] | [
"Graph Mining"
] | 1,556,150,400,000 | [] | 47,814 |
197,744 | https://paperswithcode.com/paper/challenges-and-approaches-to-time-series | 2101.04224 | Challenges and approaches to time-series forecasting in data center telemetry: A Survey | Time-series forecasting has been an important research domain for so many years. Its applications include ECG predictions, sales forecasting, weather conditions, even COVID-19 spread predictions. These applications have motivated many researchers to figure out an optimal forecasting approach, but the modeling approach ... | https://arxiv.org/abs/2101.04224v2 | https://arxiv.org/pdf/2101.04224v2.pdf | null | [
"Shruti Jadon",
"Jan Kanty Milczek",
"Ajit Patankar"
] | [
"Time Series",
"Time Series Forecasting"
] | 1,610,323,200,000 | [] | 130,720 |
244,872 | https://paperswithcode.com/paper/meta-attack-class-agnostic-and-model-agnostic | null | Meta-Attack: Class-Agnostic and Model-Agnostic Physical Adversarial Attack | Modern deep neural networks are often vulnerable to adversarial examples. Most exist attack methods focus on crafting adversarial examples in the digital domain, while only limited works study physical adversarial attack. However, it is more challenging to generate effective adversarial examples in the physical wor... | http://openaccess.thecvf.com//content/ICCV2021/html/Feng_Meta-Attack_Class-Agnostic_and_Model-Agnostic_Physical_Adversarial_Attack_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Feng_Meta-Attack_Class-Agnostic_and_Model-Agnostic_Physical_Adversarial_Attack_ICCV_2021_paper.pdf | ICCV 2021 10 | [
"Weiwei Feng",
"Baoyuan Wu",
"Tianzhu Zhang",
"Yong Zhang",
"Yongdong Zhang"
] | [
"Adversarial Attack",
"Few-Shot Learning",
"Meta-Learning"
] | 1,609,459,200,000 | [] | 78,015 |
292,705 | https://paperswithcode.com/paper/attribution-based-task-specific-pruning-for | 2205.04157 | Attribution-based Task-specific Pruning for Multi-task Language Models | Multi-task language models show outstanding performance for various natural language understanding tasks with only a single model. However, these language models inevitably utilize unnecessary large-scale model parameters, even when they are used for only a specific task. In this paper, we propose a novel training-free... | https://arxiv.org/abs/2205.04157v1 | https://arxiv.org/pdf/2205.04157v1.pdf | null | [
"Nakyeong Yang",
"Yunah Jang",
"Hwanhee Lee",
"Seohyeong Jung",
"Kyomin Jung"
] | [
"Natural Language Understanding"
] | 1,652,054,400,000 | [] | 126,228 |
237,956 | https://paperswithcode.com/paper/deep-learning-of-transferable-mimo-channel | 2108.13831 | Deep Learning of Transferable MIMO Channel Modes for 6G V2X Communications | In the emerging high mobility Vehicle-to-Everything (V2X) communications using millimeter Wave (mmWave) and sub-THz, Multiple-Input Multiple-Output (MIMO) channel estimation is an extremely challenging task. At mmWaves/sub-THz frequencies, MIMO channels exhibit few leading paths in the space-time domain (i.e., directio... | https://arxiv.org/abs/2108.13831v1 | https://arxiv.org/pdf/2108.13831v1.pdf | null | [
"Lorenzo Cazzella",
"Dario Tagliaferri",
"Marouan Mizmizi",
"Damiano Badini",
"Christian Mazzucco",
"Matteo Matteucci",
"Umberto Spagnolini"
] | [
"Transfer Learning"
] | 1,630,368,000,000 | [] | 158,899 |
301,905 | https://paperswithcode.com/paper/the-open-catalyst-2022-oc22-dataset-and | 2206.08917 | The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysis | Computational catalysis and machine learning communities have made considerable progress in developing machine learning models for catalyst discovery and design. Yet, a general machine learning potential that spans the chemical space of catalysis is still out of reach. A significant hurdle is obtaining access to traini... | https://arxiv.org/abs/2206.08917v1 | https://arxiv.org/pdf/2206.08917v1.pdf | null | [
"Richard Tran",
"Janice Lan",
"Muhammed Shuaibi",
"Siddharth Goyal",
"Brandon M. Wood",
"Abhishek Das",
"Javier Heras-Domingo",
"Adeesh Kolluru",
"Ammar Rizvi",
"Nima Shoghi",
"Anuroop Sriram",
"Zachary Ulissi",
"C. Lawrence Zitnick"
] | [
"Total Energy"
] | 1,655,424,000,000 | [] | 75,420 |
247,054 | https://paperswithcode.com/paper/learning-invariant-representations-on | null | Learning Invariant Representations on Multilingual Language Models for Unsupervised Cross-Lingual Transfer | Recent advances in neural modeling have produced deep multilingual language models capable of extracting cross-lingual knowledge from unparallel texts, as evidenced by their decent zero-shot transfer performance. While analyses have attributed this success to having cross-lingually shared representations, its contribut... | https://openreview.net/forum?id=k7-s5HSSPE5 | https://openreview.net/pdf?id=k7-s5HSSPE5 | ICLR 2022 4 | [
"Ruicheng Xian",
"Heng Ji",
"Han Zhao"
] | [
"Cross-Lingual Transfer",
"Domain Adaptation"
] | 1,632,873,600,000 | [] | 117,013 |
293,181 | https://paperswithcode.com/paper/from-distillation-to-hard-negative-sampling | 2205.04733 | From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective | Neural retrievers based on dense representations combined with Approximate Nearest Neighbors search have recently received a lot of attention, owing their success to distillation and/or better sampling of examples for training -- while still relying on the same backbone architecture. In the meantime, sparse representat... | https://arxiv.org/abs/2205.04733v2 | https://arxiv.org/pdf/2205.04733v2.pdf | null | [
"Thibault Formal",
"Carlos Lassance",
"Benjamin Piwowarski",
"Stéphane Clinchant"
] | [
"Language Modelling",
"Representation Learning"
] | 1,652,140,800,000 | [] | 99,811 |
103,510 | https://paperswithcode.com/paper/sequence-tagging-with-contextual-and-non | 1906.01569 | Sequence Tagging with Contextual and Non-Contextual Subword Representations: A Multilingual Evaluation | Pretrained contextual and non-contextual subword embeddings have become available in over 250 languages, allowing massively multilingual NLP. However, while there is no dearth of pretrained embeddings, the distinct lack of systematic evaluations makes it difficult for practitioners to choose between them. In this work,... | https://arxiv.org/abs/1906.01569v1 | https://arxiv.org/pdf/1906.01569v1.pdf | ACL 2019 7 | [
"Benjamin Heinzerling",
"Michael Strube"
] | [
"Multilingual Named Entity Recognition",
"Multilingual NLP",
"Named Entity Recognition",
"Named Entity Recognition",
"Part-Of-Speech Tagging"
] | 1,559,606,400,000 | [
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/7c077f6a986f05383bcb86b535aedb5a63dd5c4b/torchvision/models/resnet.py#L118",
"description": "**Residual Connections** are a type of skip-connection that learn residual functions with reference to the layer inputs, instead of learning unreferenced... | 30,210 |
293,482 | https://paperswithcode.com/paper/group-r-cnn-for-weakly-semi-supervised-object | 2205.05920 | Group R-CNN for Weakly Semi-supervised Object Detection with Points | We study the problem of weakly semi-supervised object detection with points (WSSOD-P), where the training data is combined by a small set of fully annotated images with bounding boxes and a large set of weakly-labeled images with only a single point annotated for each instance. The core of this task is to train a point... | https://arxiv.org/abs/2205.05920v1 | https://arxiv.org/pdf/2205.05920v1.pdf | CVPR 2022 1 | [
"Shilong Zhang",
"Zhuoran Yu",
"Liyang Liu",
"Xinjiang Wang",
"Aojun Zhou",
"Kai Chen"
] | [
"Object Detection",
"Object Detection",
"Representation Learning",
"Semi-Supervised Object Detection"
] | 1,652,313,600,000 | [
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/b7bda236d18815052378c88081f64935427d7716/torch/optim/adam.py#L6",
"description": "**Adam** is an adaptive learning rate optimization algorithm that utilises both momentum and scaling, combining the benefits of [RMSProp](https://paperswithcode.co... | 6,235 |
264,807 | https://paperswithcode.com/paper/contrastive-object-level-pre-training-with | 2111.13651 | Contrastive Object-level Pre-training with Spatial Noise Curriculum Learning | The goal of contrastive learning based pre-training is to leverage large quantities of unlabeled data to produce a model that can be readily adapted downstream. Current approaches revolve around solving an image discrimination task: given an anchor image, an augmented counterpart of that image, and some other images, t... | https://arxiv.org/abs/2111.13651v2 | https://arxiv.org/pdf/2111.13651v2.pdf | null | [
"Chenhongyi Yang",
"Lichao Huang",
"Elliot J. Crowley"
] | [
"Contrastive Learning",
"Instance Segmentation",
"Semantic Segmentation"
] | 1,637,884,800,000 | [
{
"code_snippet_url": null,
"description": "",
"full_name": null,
"introduced_year": 2000,
"main_collection": {
"area": "Graphs",
"description": "",
"name": "Graph Representation Learning",
"parent": null
},
"name": "Contrastive Learning",
"source_title": null... | 98,570 |
292,881 | https://paperswithcode.com/paper/on-generalisability-of-machine-learning-based | 2205.04112 | On Generalisability of Machine Learning-based Network Intrusion Detection Systems | Many of the proposed machine learning (ML) based network intrusion detection systems (NIDSs) achieve near perfect detection performance when evaluated on synthetic benchmark datasets. Though, there is no record of if and how these results generalise to other network scenarios, in particular to real-world networks. In t... | https://arxiv.org/abs/2205.04112v1 | https://arxiv.org/pdf/2205.04112v1.pdf | null | [
"Siamak Layeghy",
"Marius Portmann"
] | [
"Intrusion Detection",
"Network Intrusion Detection"
] | 1,652,054,400,000 | [
{
"code_snippet_url": "https://github.com/slundberg/shap",
"description": "**SHAP**, or **SHapley Additive exPlanations**, is a game theoretic approach to explain the output of any machine learning model. It connects optimal credit allocation with local explanations using the classic Shapley values from gam... | 139,621 |
7,950 | https://paperswithcode.com/paper/a-temporally-aware-interpolation-network-for | 1803.07218 | A Temporally-Aware Interpolation Network for Video Frame Inpainting | We propose the first deep learning solution to video frame inpainting, a
challenging instance of the general video inpainting problem with applications
in video editing, manipulation, and forensics. Our task is less ambiguous than
frame interpolation and video prediction because we have access to both the
temporal cont... | http://arxiv.org/abs/1803.07218v2 | http://arxiv.org/pdf/1803.07218v2.pdf | null | [
"Ximeng Sun",
"Ryan Szeto",
"Jason J. Corso"
] | [
"Video Inpainting",
"Video Prediction"
] | 1,521,504,000,000 | [] | 77,074 |
293,315 | https://paperswithcode.com/paper/generalized-fast-multichannel-nonnegative | 2205.05330 | Generalized Fast Multichannel Nonnegative Matrix Factorization Based on Gaussian Scale Mixtures for Blind Source Separation | This paper describes heavy-tailed extensions of a state-of-the-art versatile blind source separation method called fast multichannel nonnegative matrix factorization (FastMNMF) from a unified point of view. The common way of deriving such an extension is to replace the multivariate complex Gaussian distribution in the ... | https://arxiv.org/abs/2205.05330v1 | https://arxiv.org/pdf/2205.05330v1.pdf | null | [
"Mathieu Fontaine",
"Kouhei Sekiguchi",
"Aditya Nugraha",
"Yoshiaki Bando",
"Kazuyoshi Yoshii"
] | [
"Speech Enhancement"
] | 1,652,227,200,000 | [] | 62,217 |
2,583 | https://paperswithcode.com/paper/a-double-deep-spatio-angular-learning | 1805.10078 | A Double-Deep Spatio-Angular Learning Framework for Light Field based Face Recognition | Face recognition has attracted increasing attention due to its wide range of
applications, but it is still challenging when facing large variations in the
biometric data characteristics. Lenslet light field cameras have recently come
into prominence to capture rich spatio-angular information, thus offering new
possibil... | http://arxiv.org/abs/1805.10078v3 | http://arxiv.org/pdf/1805.10078v3.pdf | null | [
"Alireza Sepas-Moghaddam",
"Mohammad A. Haque",
"Paulo Lobato Correia",
"Kamal Nasrollahi",
"Thomas B. Moeslund",
"Fernando Pereira"
] | [
"Face Recognition"
] | 1,527,206,400,000 | [
{
"code_snippet_url": null,
"description": "",
"full_name": "VGG-16",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Convolutional Neural Networks** are used to extract features from images (and videos), employing convolutions as their prim... | 75,209 |
181,256 | https://paperswithcode.com/paper/multiple-time-series-ising-model-for | 1611.08088 | Multiple Time Series Ising Model for Financial Market Simulations | In this paper we propose an Ising model which simulates multiple financial
time series. Our model introduces the interaction which couples to spins of
other systems. Simulations from our model show that time series exhibit the
volatility clustering that is often observed in the real financial markets.
Furthermore we al... | http://arxiv.org/abs/1611.08088v1 | http://arxiv.org/pdf/1611.08088v1.pdf | null | [] | [
"Time Series"
] | 1,479,945,600,000 | [] | 171,106 |
230,567 | https://paperswithcode.com/paper/a-survey-on-low-resource-neural-machine | 2107.04239 | A Survey on Low-Resource Neural Machine Translation | Neural approaches have achieved state-of-the-art accuracy on machine translation but suffer from the high cost of collecting large scale parallel data. Thus, a lot of research has been conducted for neural machine translation (NMT) with very limited parallel data, i.e., the low-resource setting. In this paper, we provi... | https://arxiv.org/abs/2107.04239v1 | https://arxiv.org/pdf/2107.04239v1.pdf | null | [
"Rui Wang",
"Xu Tan",
"Renqian Luo",
"Tao Qin",
"Tie-Yan Liu"
] | [
"Low-Resource Neural Machine Translation",
"Machine Translation"
] | 1,625,788,800,000 | [] | 157,275 |
118,278 | https://paperswithcode.com/paper/unsupervised-representation-for-ehr-signals | 1910.01803 | Unsupervised Representation for EHR Signals and Codes as Patient Status Vector | Effective modeling of electronic health records presents many challenges as they contain large amounts of irregularity most of which are due to the varying procedures and diagnosis a patient may have. Despite the recent progress in machine learning, unsupervised learning remains largely at open, especially in the healt... | https://arxiv.org/abs/1910.01803v1 | https://arxiv.org/pdf/1910.01803v1.pdf | null | [
"Sajad Darabi",
"Mohammad Kachuee",
"Majid Sarrafzadeh"
] | [
"Representation Learning",
"Time Series"
] | 1,570,147,200,000 | [] | 140,956 |
75,823 | https://paperswithcode.com/paper/no-reference-color-image-quality-assessment | 1812.10695 | No-Reference Color Image Quality Assessment: From Entropy to Perceptual Quality | This paper presents a high-performance general-purpose no-reference (NR)
image quality assessment (IQA) method based on image entropy. The image
features are extracted from two domains. In the spatial domain, the mutual
information between the color channels and the two-dimensional entropy are
calculated. In the freque... | http://arxiv.org/abs/1812.10695v1 | http://arxiv.org/pdf/1812.10695v1.pdf | null | [
"Xiaoqiao Chen",
"Qingyi Zhang",
"Manhui Lin",
"Guangyi Yang",
"Chu He"
] | [
"Image Quality Assessment",
"No-Reference Image Quality Assessment"
] | 1,545,868,800,000 | [] | 27,746 |
3,503 | https://paperswithcode.com/paper/understanding-and-improving-deep-neural | 1805.07020 | Understanding and Improving Deep Neural Network for Activity Recognition | Activity recognition has become a popular research branch in the field of
pervasive computing in recent years. A large number of experiments can be
obtained that activity sensor-based data's characteristic in activity
recognition is variety, volume, and velocity. Deep learning technology,
together with its various mode... | http://arxiv.org/abs/1805.07020v1 | http://arxiv.org/pdf/1805.07020v1.pdf | null | [
"Li Xue",
"Si Xiandong",
"Nie Lanshun",
"Li Jiazhen",
"Ding Renjie",
"Zhan Dechen",
"Chu Dianhui"
] | [
"Activity Recognition",
"Classification",
"Human Activity Recognition"
] | 1,526,601,600,000 | [
{
"code_snippet_url": null,
"description": "A **convolution** is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the part of the input it is on, then summing the results into an output.\r\n\r\nIntuitively,... | 82,791 |
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