Shibani Santurkar | DeepAI

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Ludwig Schmidt
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∙ 03/30/2023

Whose Opinions Do Language Models Reflect?

Language models (LMs) are increasingly being used in open-ended contexts...

0 Shibani Santurkar, et al. ∙

∙ 02/06/2023

Data Selection for Language Models via Importance Resampling

Selecting a suitable training dataset is crucial for both general-domain...

0 Sang Michael Xie, et al. ∙

∙ 11/16/2022

Holistic Evaluation of Language Models

Language models (LMs) are becoming the foundation for almost all major l...

21 Percy Liang, et al. ∙

∙ 12/02/2021

Editing a classifier by rewriting its prediction rules

We present a methodology for modifying the behavior of a classifier by d...

13 Shibani Santurkar, et al. ∙

∙ 05/11/2021

Leveraging Sparse Linear Layers for Debuggable Deep Networks

We show how fitting sparse linear models over learned deep feature repre...

12 Eric Wong, et al. ∙

∙ 08/11/2020

BREEDS: Benchmarks for Subpopulation Shift

We develop a methodology for assessing the robustness of models to subpo...

5 Shibani Santurkar, et al. ∙

∙ 05/25/2020

Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO

We study the roots of algorithmic progress in deep policy gradient algor...

0 Logan Engstrom, et al. ∙

∙ 05/22/2020

From ImageNet to Image Classification: Contextualizing Progress on Benchmarks

Building rich machine learning datasets in a scalable manner often neces...

24 Dimitris Tsipras, et al. ∙

∙ 05/19/2020

Identifying Statistical Bias in Dataset Replication

Dataset replication is a useful tool for assessing whether improvements ...

10 Logan Engstrom, et al. ∙

∙ 06/06/2019

Image Synthesis with a Single (Robust) Classifier

We show that the basic classification framework alone can be used to tac...

0 Shibani Santurkar, et al. ∙

∙ 06/06/2019

Computer Vision with a Single (Robust) Classifier

We show that the basic classification framework alone can be used to tac...

0 Shibani Santurkar, et al. ∙

∙ 06/03/2019

Learning Perceptually-Aligned Representations via Adversarial Robustness

Many applications of machine learning require models that are human-alig...

0 Logan Engstrom, et al. ∙

∙ 05/06/2019

Adversarial Examples Are Not Bugs, They Are Features

Adversarial examples have attracted significant attention in machine lea...

0 Andrew Ilyas, et al. ∙

∙ 11/06/2018

Are Deep Policy Gradient Algorithms Truly Policy Gradient Algorithms?

We study how the behavior of deep policy gradient algorithms reflects th...

4 Andrew Ilyas, et al. ∙

∙ 05/30/2018

There Is No Free Lunch In Adversarial Robustness (But There Are Unexpected Benefits)

We provide a new understanding of the fundamental nature of adversariall...

2 Dimitris Tsipras, et al. ∙

∙ 05/29/2018

How Does Batch Normalization Help Optimization? (No, It Is Not About Internal Covariate Shift)

Batch Normalization (BatchNorm) is a widely adopted technique that enabl...

0 Shibani Santurkar, et al. ∙

∙ 04/30/2018

Adversarially Robust Generalization Requires More Data

Machine learning models are often susceptible to adversarial perturbatio...

0 Ludwig Schmidt, et al. ∙

∙ 11/02/2017

A Classification-Based Perspective on GAN Distributions

A fundamental, and still largely unanswered, question in the context of ...

0 Shibani Santurkar, et al. ∙

∙ 03/04/2017

Generative Compression

Traditional image and video compression algorithms rely on hand-crafted ...

0 Shibani Santurkar, et al. ∙

∙ 02/23/2017

Toward Streaming Synapse Detection with Compositional ConvNets

Connectomics is an emerging field in neuroscience that aims to reconstru...

0 Shibani Santurkar, et al. ∙

∙ 11/20/2016

Deep Tensor Convolution on Multicores

Deep convolutional neural networks (ConvNets) of 3-dimensional kernels a...

0 David Budden, et al. ∙

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