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# Aspest: Bridging The Gap Between Active Learning And Selective Prediction
Jiefeng Chen∗*jiefeng@cs.wisc.edu* University of Wisconsin-Madison Jinsung Yoon jinsungyoon@google.com Google Sayna Ebrahimi *saynae@google.com* Google Sercan Ö. Arık soarik@google.com Google Somesh Jha jha@cs.wisc.edu University of Wisconsin-... | Review 1:
Summary: This paper introduces a new learning paradigm called active selective prediction that combines selective prediction (not predicting on uncertain points) and active learning (deciding which datapoints to obtain human labels for). The authors' new framework aims to query more informative sample that th... |
# Centroids Matching: An Efficient Continual Learning Approach Operating In The Embedding Space
Jary Pomponi *jary.pomponi@uniroma1.it* Department of Information Engineering, Sapienza University of Rome, Italy Simone Scardapane *simone.scardapane@uniroma1.it* Department of Information Engineering, Sapienza University ... | Review 1:
Summary: **Problem**:
This work addresses the problem of continual learning *i.e.* learning a sequence of tasks without forgetting/deteriorating performance on the previous. The work focuses on the standard setting of a sequence of image classification tasks.
**Solution**: The paper proposes a solution call... |
# Complementary Sparsity: Accelerating Sparse Cnns With High Accuracy On General-Purpose Computing Platforms
Yijun Tan∗*tanyj1998@gmail.com* SKL of Processors, Institute of Computing Technology, CAS
Yunhe Wang†*yunhe.wang@huawei.com* Huawei Noah Ark's Lab Jun Yao†yaojun97@huawei.com Huawei Noah Ark's Lab
Kang Zhao∗*z... | Review 1:
Summary: The manuscript introduces a novel fine-grained pruning technique termed 'complementary sparsity,' aimed at boosting model accuracy while preserving parallelism for enhanced performance on standard CPUs and GPUs. Through comparisons with existing coarse-grained and fine-grained pruning approaches, the... |
# Robust Stochastic Optimization Via Gradient Quantile Clipping
Anonymous authors May 9, 2024
## Abstract
We introduce a clipping strategy for Stochastic Gradient Descent (SGD) which uses quantiles of the gradient norm as clipping thresholds. We prove that this new strategy provides a robust and efficient optimizati... | Review 1:
Summary: The work proposes a new variant of clipped SGD with the choice of clipping threshold based on quantiles of stochastic gradients. The convergence of this algorithm in distribution is studied for strongly convex and non-convex problems. Numerical results complement the study.
Strengths and Weaknesses:... |
# Dr-Dsgd: A Distributionally Robust Decentralized Learning Algorithm Over Graphs
Chaouki Ben Issaid *chaouki.benissaid@oulu.fi* Centre for Wireless Communications University of Oulu, Finland Anis Elgabli *anis.elgabli@oulu.fi* Centre for Wireless Communications University of Oulu, Finland Mehdi Bennis *mehdi.bennis@o... | Review 1:
Summary: This works studies distributionally robust optimization, i.e., the problem of minimizing the worst linear combination of given losses $f_i$, where $f_i$ is the objective of $i$-th device. The authors assume that $f_i$ is given in the form of expectation over random samples, with bounded values, bound... |
# Towards More Robust Nlp System Evaluation: Handling Missing Scores In Benchmarks
Anonymous authors Paper under double-blind review
## Abstract
The evaluation of natural language processing (NLP) systems is crucial for advancing the field, but current benchmarking approaches often assume that all systems have score... | Review 1:
Summary: The paper tackles an existing problem in benchmarks of any ml systems: benchmarking with missing scores. The proposed method utilizes a compatible partial ranking approach to impute missing data, which is then aggregated using the Borda count method. The method considers both task-level and instance-... |
# Unifying Pixel-Labeling Vision Tasks By Sequence Modeling
Anonymous authors Paper under double-blind review
## Abstract
Developing a single neural network that can perform a wide range of tasks is an active area of research in computer vision. However, unifying models for pixel-labeling tasks presents significant ... | Review 1:
Summary: This work proposes a unified architecture, UniTask, for three pixel-wise tasks: semantic segmentation, surface normal estimation, and monocular depth estimation.
The same encoder, latent coding, and decoder modules are applied to each task.
The encoding backbone is taken from prior work, but the enco... |
# Dropped Scheduled Task: Mitigating Negative Transfer In Multi-Task Learning Using Dynamic Task Dropping
Aakarsh Malhotra aakarshm@iiitd.ac.in Department of Computer Science IIIT-Delhi, New Delhi, India 110020 Mayank Vatsa mvatsa@iitj.ac.in IIT Jodhpur, Rajasthan, India 342037 Richa Singh richa@iitj.ac.in IIT Jodhpur... | Review 1:
Summary: This paper proposes a multi-task learning framework, which differs from previous work in that the authors define several metrics for "dropping" the task in the multi-task training framework so that the tasks are dynamically dropped. The authors claim that this dropped scheduled task can help remove t... |
# Chasing Better Deep Image Priors Between Over- And Under-Parameterization
Qiming Wu *qimingwu@cs.ucsb.edu* University of California, Santa Barbara Xiaohan Chen *xiaohan.chen@alibaba-inc.com* Decision Intelligence Lab, Damo Academy, Alibaba Group (U.S.)
Yifan Jiang *yifanjiang97@utexas.edu* University of Texas at Aus... | Review 1:
Summary: The paper proposes to prune the over-parameterized image priors to find a better performing and efficient image prior based on the Lottery Ticket Hypothesis. The pruned image prior results in better quality solutions to various inverse problems addressed by the DIP, with the relatively small number o... |
# On The Analysis And Reproduction Of "Post-Hoc Concept Bottleneck Models" With An Extension To The Audio Domain
Anonymous authors Paper under double-blind review
## Abstract
Although deep neural networks are powerful tools, they are yet considered "black boxes". With the proliferation of AI models, the need for the... | Review 1:
Summary: This paper investigates the prior work on Post-hoc Concept Bottleneck Models (PCBMs) and their ability to enhance the interpretability of deep neural networks while maintaining high performance. This involves applying the PCBM approach to image classification tasks using the same datasets and backbon... |
# Measuring Orthogonality In Representations Of Generative Models
Anonymous authors Paper under double-blind review
## Abstract
In unsupervised representation learning, models aim to distill essential features from highdimensional data into lower-dimensional learned representations, guided by inductive biases.
Unde... | Review 1:
Summary: The paper proposes two new metrics, Importance-Weighted Orthogonality (IWO) and Importance-Weighted Rank (IWR), to evaluate the disentanglement of representations, as the existing methods fail to address the issue in Fig. 1, where some certain bases are better than others to capture the disentangleme... |
# Federated Variational Inference: Towards Improved Personalization And Generalization
Elahe Vedadi *elahevedadi@google.com* Google Research Joshua V. Dillon *jvdillon@google.com* Google Research Philip Andrew Mansfield memes@google.com Google Research Karan Singhal karansinghal@google.com Google Research Arash Afkanp... | Review 1:
Summary: This paper introduces Federated Variational Inference (FedVI), a novel algorithm designed specifically to enhance personalization and generalization in federated learning (FL). The authors claim that FedVI more effectively handles data heterogeneity. The FL process is formulated using a hierarchical ... |
# Towards Fully Covariant Machine Learning
Soledad Villar∗*soledad.villar@jhu.edu* Department of Applied Mathematics and Statistics, Johns Hopkins University Mathematical Institute for Data Science, Johns Hopkins University Flatiron Institute, a division of the Simons Foundation David W. Hogg∗*david.hogg@nyu.edu* Cent... | Review 1:
Summary: The paper considers the problem of building predictive models that are equivariant with respect to passive symmetries, i.e. symmetries that arise from arbitrary choices such as coordinate systems. These are distinct from so called "active" symmetries which are (often approximate) empirically verified... |
# Fair And Useful Cohort Selection
Konstantina Bairaktari *bairaktari.k@northeastern.edu* Khoury College of Computer Sciences Northeastern University Paul Langton *langton.p@northeastern.edu* Khoury College of Computer Sciences Northeastern University Huy Le Nguyen *hu.nguyen@northeastern.edu* Khoury College of Comput... | Review 1:
Summary: The paper studies the problem of cohort selection, i.e. selecting $k$ candidates out of $n$, which achieves both fairness and utility. This problem is studied in the offline and online full-information setting, and with linear and ratio utilities. Specifically, the approach considered is to compose p... |
# Test-Time Adaptation For Visual Document Understanding
Sayna Ebrahimi saynae@google.com Google Cloud AI Research
Sercan Ö. Arik soarik@google.com
Google Cloud AI Research
Tomas Pfister *tpfister@google.com* Google Cloud AI Research Reviewed on OpenReview: *https: // openreview. net/ forum? id= zshemTAa6U*
## Abs... | Review 1:
Summary: - This work introduces a new benchmark for domain adaptation for document understanding tasks such as form understanding and VQA.
- As the authors observe domain adaptation for document images understanding is not a well studied problem. Authors show that a simple in-domain self supervised training... |
# What Should A (Future) Deep Learning Theory Look Like? A Phenomenological Perspective
Anonymous authors Paper under double-blind review September 9, 2022
## Abstract
To advance deep learning methodologies in the next decade, a theoretical framework for reasoning about modern neural networks is needed. While effort... | Review 1:
Summary: This paper proposes a new model for deep learning, termed the _neurashed_. This model shares some important features of neural networks, in particular their hierarchical structure, and can be trained via an iterative procedure that resembles stochastic gradient descent. The paper goes on to replicate... |
# Diffusion Model-Augmented Behavioral Cloning
Anonymous authors Paper under double-blind review
## Abstract
Imitation learning addresses the challenge of learning by observing an expert's demonstrations without access to reward signals from environments. Most existing imitation learning methods that do not require ... | |
# Simple Drop-In Lora Conditioning On Attention Layers Will Improve Your Diffusion Model
Anonymous authors Paper under double-blind review
Figure 1: The standard U-Net architecture for diffusion models conditions convolutional layers in residual blocks with scale-and-shift but does no... | Review 1:
Summary: This work examines the problem of including scalar/vector conditional information such as timestep and class label in the attention layers of UNet-based diffusion models. The majority of current methods include this type of conditional information only in the residual layer of the UNet, which seems s... |
# Dcp: Learning Accelerator Dataflow For Neural Network Via Propagation
Anonymous authors Paper under double-blind review
## Abstract
Deep neural network (DNN) hardware (HW) accelerators have achieved great success in improving DNNs' performance and efficiency. One key reason is dataflow in executing a DNN
layer, in... | Review 1:
Summary: This work introduces Dataflow Code Propagation (DCP), an approach to derive optimal dataflows for DNN layer, that take into account parallelization of the operations across processing units, partitioning over multiple memory levels, and order of the computations. The core idea is to train an attentio... |
# Referential Communication In Heterogeneous Communities Of Pre-Trained Visual Deep Networks
Anonymous authors Paper under double-blind review
## Abstract
As large pre-trained image-processing neural networks are being embedded in autonomous agents such as self-driving cars or robots, the question arises of how such... | Review 1:
Summary: This paper contributes by systematically exploring how diverse pre-trained visual networks autonomously develop shared communication protocols despite architectural differences, demonstrating their adaptability to unseen object categories and ease of protocol acquisition, while also analyzing the eme... |
# Dataset Distillation In Large Data Era
Anonymous authors Paper under double-blind review
## Abstract
Dataset distillation or condensation aims to generate a smaller but representative subset from a large dataset, which allows a model to be trained more efficiently, meanwhile evaluating on the original testing data... | Review 1:
Summary: This paper proposes to improve large-scale dataset distillation using a proposed curriculum data augmentation (CDA), which gradually increases the training difficulty by lowering the minimal crop ratio.
The rest of the method is the same as a prior method SRe$^2$L. There are 3 steps:
- **Squeeze**, ... |
# Learning Algorithms For Markovian Bandits: Is Posterior Sampling More Scalable Than Optimism?
Nicolas Gast *nicolas.gast@inria.fr* Bruno Gaujal *bruno.gaujal@inria.fr* Kimang Khun kimang.khun@inria.fr Univ. Grenoble Alpes, Inria, CNRS, Grenoble INPú, LIG 38000 Grenoble, France ú*Institute of Engineering Univ. Grenob... | Review 1:
Summary: This paper studies Markovian bandit problems where
a) arm state transitions are restful;
b) rewards come with an expoential discounting factor $\beta$;
c) the time-horizen length in each episode is i.i.d. geometrically distributed with parameter $1-\beta$.
In the paper, three new algorithms MB-PS... |
# Dependency Structure Search Bayesian Optimization For Decision Making Models
Anonymous authors Paper under double-blind review
## Abstract
Many approaches for optimizing decision making models rely on gradient based methods requiring informative feedback from the environment. However, in the case where such feedba... | Review 1:
Summary: The authors utilize Bayesian optimization (BO) for high-dimensional multi-agent policy search (MAPS), as gradient-based methods can suffer from excessive computational requirements and sparse rewards, as well as the existence of local maxima. The authors mitigate the computational complexity of such ... |
# Read Between The Layers: Leveraging Multi-Layer Representations For Rehearsal-Free Continual Learning With Pretrained Models
Kyra Ahrens∗kyra.ahrens@uni-hamburg.de University of Hamburg Hans Hergen Lehmann∗hergen.lehmann@studium.uni-hamburg.de University of Hamburg Jae Hee Lee jae.hee.lee@uni-hamburg.de University o... | Review 1:
Summary: This paper proposed a simple approach by using the features from middle layers for rehearsal free continuous learning with a pretrained model. This work improved previous work RanPAC by using features from multiple layers without random projection to achieve better results with less computations.
Th... |
# Flexecontrol: Flexible And Efficient Multimodal Control For Text-To-Image Generation
Anonymous authors Paper under double-blind review
## Abstract
Controllable text-to-image (T2I) diffusion models generate images conditioned on both text prompts and semantic inputs of other modalities like edge maps. Nevertheless,... | Review 1:
Summary: This paper targets for flexible and efficient controllable text-to-image generation. To achieve that, the paper proposes a weight decomposition method to allow for streamlined integration of various input conditions. Experiments demonstrate that it can reduce computational resources and support vario... |
# Loc-Facmac: Locality Based Factorized Multi-Agent Actorcritic Algorithm For Cooperative Tasks
Anonymous authors Paper under double-blind review
## Abstract
In this work, we present a novel cooperative multi-agent reinforcement learning method called Locality based Factorized Multi-Agent Actor-Critic (Loc-FACMAC). ... | Review 1:
Summary: This paper extends the idea of LOMAQ to FACMAC and presents a Locality-based Factorized Multi-Agent Actor-Critic (Loc-FACMAC) framework. The core idea is to manually break the global multiagent learning problem into a set of local multiagent learning problems based on the predefined locality structur... |
# Recurrent Inertial Graph-Based Estimator (Ring): A Single Pluripotent Inertial Motion Tracking Solution
Anonymous authors Paper under double-blind review
## Abstract
This paper introduces a novel ML-based method for Inertial Motion Tracking (IMT) that fundamentally changes the way this technology is used. The prop... | Review 1:
Summary: The paper presents a novel method for Inertial Motion Tracking (IMT) called the Recurrent Inertial Graph-Based Estimator (RING). This method aims to revolutionize the field of IMT by providing a versatile, problem-unspecific solution that does not require expert knowledge for implementation. RING uti... |
# Efficient Model-Based Multi-Agent Mean-Field Reinforcement Learning
Barna Pásztor barna.pasztor@ai.ethz.ch ETH Zürich Ilija Bogunovic i.bogunovic@ucl.ac.uk University College London Andreas Krause *krausea@ethz.ch* ETH Zürich Reviewed on OpenReview: *https: // openreview. net/ forum? id= gvcDSDYUZx*
## Abstract
Le... | Review 1:
Summary: The authors study multi-agent reinforcement learning, wherein they tackle the mean-field control problem. This formulation assumes an asymptotically infinite population of agents interacting with a common environment and aiming to collaboratively maximize a collective reward. A model-based algorithm... |
# Distributed Newton-Type Methods With Communication Compression And Bernoulli Aggregation
Rustem Islamov rustem.islamov@ip-paris.fr Institut Polytechnique de Paris Palaiseau, France Xun Qian JD Explore Academy Beijing, China Slavomír Hanzely King Abdullah University of Science and Technology Thuwal, Saudi Arabia Mher... | Review 1:
Summary: This paper discusses Newton-type convex optimization methods in a distributed setting. Specifically, the authors consider a general approach using three point compressors for communicating Hessian information. A variety of compressors and aggregation schemes are explored, and two new techniques for d... |
# Fairness Via In-Processing In The Over-Parameterized Regime: A Cautionary Tale With Mindiff Loss
Akshaj Kumar Veldanda akv275@nyu.edu Electrical and Computer Engineering Department New York University Ivan Brugere∗*ivan.brugere@jpmchase.com* JP Morgan Chase AI Research Jiahao Chen∗cjiahao@gmail.com Parity Sanghamitr... | Review 1:
Summary: As the deep neural networks are increasingly being used in real-world applications, the fairness issue has also received increasing attention from the community. This work studies one widely used fairness algorithm, i.e,.MinDiff, and has some interesting findings. First, under-parameterized MinDiff m... |
# Bayesian Transformed Gaussian Processes
Xinran Zhu *xz584@cornell.edu* Cornell University, Center for Applied Mathematics
Leo Huang∗ah839@cornell.edu
Cornell University, Department of Computer Science
Eric Hans Lee∗eric.lee@intel.com SigOpt: An Intel Company Cameron Ibrahim *cibrahim@udel.edu* University of Delaw... | Review 1:
Summary: This work revisits the BTG model, which has latent and observations spaces with a deterministic function mapping these two spaces. The central idea is to approximate the BTG posterior predictive density using a mixture of t-distributions. In doing so, several tricks are cleverly intermixed to address... |
# Learning Predictive Checklists With Probabilistic Logic Programming
Anonymous authors Paper under double-blind review
## Abstract
Checklists have been widely recognized as effective tools for completing complex tasks in a systematic manner. Although originally intended for use in procedural tasks, their interpreta... |
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