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06/01/2023
Understanding Augmentation-based Self-Supervised Representation Learning via RKHS Approximation
Good data augmentation is one of the key factors that lead to the empiri...
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04/21/2023
Low-Variance Gradient Estimation in Unrolled Computation Graphs with ES-Single
We propose an evolution strategies-based algorithm for estimating gradie...
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03/01/2023
Single Image Backdoor Inversion via Robust Smoothed Classifiers
Backdoor inversion, the process of finding a backdoor trigger inserted i...
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02/02/2023
Provably Bounding Neural Network Preimages
Most work on the formal verification of neural networks has focused on b...
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12/08/2022
Learning Options via Compression
Identifying statistical regularities in solutions to some tasks in multi...
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12/01/2022
Finetune like you pretrain: Improved finetuning of zero-shot vision models
Finetuning image-text models such as CLIP achieves state-of-the-art accu...
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11/18/2022
Path Independent Equilibrium Models Can Better Exploit Test-Time Computation
Designing networks capable of attaining better performance with an incre...
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10/26/2022
Improving Adversarial Robustness via Joint Classification and Multiple Explicit Detection Classes
This work concerns the development of deep networks that are certifiably...
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10/23/2022
Deep Equilibrium Approaches to Diffusion Models
Diffusion-based generative models are extremely effective in generating ...
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10/13/2022
Efficiently Computing Local Lipschitz Constants of Neural Networks via Bound Propagation
Lipschitz constants are connected to many properties of neural networks,...
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09/01/2022
Unified Fully and Timestamp Supervised Temporal Action Segmentation via Sequence to Sequence Translation
This paper introduces a unified framework for video action segmentation ...
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07/20/2022
Test-Time Adaptation via Conjugate Pseudo-labels
Test-time adaptation (TTA) refers to adapting neural networks to distrib...
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06/27/2022
Agreement-on-the-Line: Predicting the Performance of Neural Networks under Distribution Shift
Recently, Miller et al. showed that a model's in-distribution (ID) accur...
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06/25/2022
Defending Multimodal Fusion Models against Single-Source Adversaries
Beyond achieving high performance across many vision tasks, multimodal m...
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01/28/2022
Understanding Why Generalized Reweighting Does Not Improve Over ERM
Empirical risk minimization (ERM) is known in practice to be non-robust ...
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10/26/2021
Boosted CVaR Classification
Many modern machine learning tasks require models with high tail perform...
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02/12/2021
Exploring Classic and Neural Lexical Translation Models for Information Retrieval: Interpretability, Effectiveness, and Efficiency Benefits
We study the utility of the lexical translation model (IBM Model 1) for ...
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01/29/2021
You Only Query Once: Effective Black Box Adversarial Attacks with Minimal Repeated Queries
Researchers have repeatedly shown that it is possible to craft adversari...
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08/13/2020
A community-powered search of machine learning strategy space to find NMR property prediction models
The rise of machine learning (ML) has created an explosion in the potent...
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04/22/2020
Provably robust deep generative models
Recent work in adversarial attacks has developed provably robust methods...
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10/28/2019
Differentiable Convex Optimization Layers
Recent work has shown how to embed differentiable optimization problems ...
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06/20/2019
On Physical Adversarial Patches for Object Detection
In this paper, we demonstrate a physical adversarial patch attack agains...
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05/29/2019