Search Results for author: Shibani Santurkar

Found 28 papers, 17 papers with code

Statistical Bias in Dataset Replication

no code implementations ICML 2020 Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Jacob Steinhardt, Aleksander Madry

Dataset replication is a useful tool for assessing whether models have overfit to a specific validation set or the exact circumstances under which it was generated.

Whose Opinions Do Language Models Reflect?

1 code implementation30 Mar 2023 Shibani Santurkar, Esin Durmus, Faisal Ladhak, Cinoo Lee, Percy Liang, Tatsunori Hashimoto

Language models (LMs) are increasingly being used in open-ended contexts, where the opinions reflected by LMs in response to subjective queries can have a profound impact, both on user satisfaction, as well as shaping the views of society at large.

GPT-4 Technical Report

9 code implementations Preprint 2023 OpenAI, :, Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, Red Avila, Igor Babuschkin, Suchir Balaji, Valerie Balcom, Paul Baltescu, Haiming Bao, Mo Bavarian, Jeff Belgum, Irwan Bello, Jake Berdine, Gabriel Bernadett-Shapiro, Christopher Berner, Lenny Bogdonoff, Oleg Boiko, Madelaine Boyd, Anna-Luisa Brakman, Greg Brockman, Tim Brooks, Miles Brundage, Kevin Button, Trevor Cai, Rosie Campbell, Andrew Cann, Brittany Carey, Chelsea Carlson, Rory Carmichael, Brooke Chan, Che Chang, Fotis Chantzis, Derek Chen, Sully Chen, Ruby Chen, Jason Chen, Mark Chen, Ben Chess, Chester Cho, Casey Chu, Hyung Won Chung, Dave Cummings, Jeremiah Currier, Yunxing Dai, Cory Decareaux, Thomas Degry, Noah Deutsch, Damien Deville, Arka Dhar, David Dohan, Steve Dowling, Sheila Dunning, Adrien Ecoffet, Atty Eleti, Tyna Eloundou, David Farhi, Liam Fedus, Niko Felix, Simón Posada Fishman, Juston Forte, Isabella Fulford, Leo Gao, Elie Georges, Christian Gibson, Vik Goel, Tarun Gogineni, Gabriel Goh, Rapha Gontijo-Lopes, Jonathan Gordon, Morgan Grafstein, Scott Gray, Ryan Greene, Joshua Gross, Shixiang Shane Gu, Yufei Guo, Chris Hallacy, Jesse Han, Jeff Harris, Yuchen He, Mike Heaton, Johannes Heidecke, Chris Hesse, Alan Hickey, Wade Hickey, Peter Hoeschele, Brandon Houghton, Kenny Hsu, Shengli Hu, Xin Hu, Joost Huizinga, Shantanu Jain, Shawn Jain, Joanne Jang, Angela Jiang, Roger Jiang, Haozhun Jin, Denny Jin, Shino Jomoto, Billie Jonn, Heewoo Jun, Tomer Kaftan, Łukasz Kaiser, Ali Kamali, Ingmar Kanitscheider, Nitish Shirish Keskar, Tabarak Khan, Logan Kilpatrick, Jong Wook Kim, Christina Kim, Yongjik Kim, Hendrik Kirchner, Jamie Kiros, Matt Knight, Daniel Kokotajlo, Łukasz Kondraciuk, Andrew Kondrich, Aris Konstantinidis, Kyle Kosic, Gretchen Krueger, Vishal Kuo, Michael Lampe, Ikai Lan, Teddy Lee, Jan Leike, Jade Leung, Daniel Levy, Chak Ming Li, Rachel Lim, Molly Lin, Stephanie Lin, Mateusz Litwin, Theresa Lopez, Ryan Lowe, Patricia Lue, Anna Makanju, Kim Malfacini, Sam Manning, Todor Markov, Yaniv Markovski, Bianca Martin, Katie Mayer, Andrew Mayne, Bob McGrew, Scott Mayer McKinney, Christine McLeavey, Paul McMillan, Jake McNeil, David Medina, Aalok Mehta, Jacob Menick, Luke Metz, Andrey Mishchenko, Pamela Mishkin, Vinnie Monaco, Evan Morikawa, Daniel Mossing, Tong Mu, Mira Murati, Oleg Murk, David Mély, Ashvin Nair, Reiichiro Nakano, Rajeev Nayak, Arvind Neelakantan, Richard Ngo, Hyeonwoo Noh, Long Ouyang, Cullen O'Keefe, Jakub Pachocki, Alex Paino, Joe Palermo, Ashley Pantuliano, Giambattista Parascandolo, Joel Parish, Emy Parparita, Alex Passos, Mikhail Pavlov, Andrew Peng, Adam Perelman, Filipe de Avila Belbute Peres, Michael Petrov, Henrique Ponde de Oliveira Pinto, Michael, Pokorny, Michelle Pokrass, Vitchyr Pong, Tolly Powell, Alethea Power, Boris Power, Elizabeth Proehl, Raul Puri, Alec Radford, Jack Rae, Aditya Ramesh, Cameron Raymond, Francis Real, Kendra Rimbach, Carl Ross, Bob Rotsted, Henri Roussez, Nick Ryder, Mario Saltarelli, Ted Sanders, Shibani Santurkar, Girish Sastry, Heather Schmidt, David Schnurr, John Schulman, Daniel Selsam, Kyla Sheppard, Toki Sherbakov, Jessica Shieh, Sarah Shoker, Pranav Shyam, Szymon Sidor, Eric Sigler, Maddie Simens, Jordan Sitkin, Katarina Slama, Ian Sohl, Benjamin Sokolowsky, Yang song, Natalie Staudacher, Felipe Petroski Such, Natalie Summers, Ilya Sutskever, Jie Tang, Nikolas Tezak, Madeleine Thompson, Phil Tillet, Amin Tootoonchian, Elizabeth Tseng, Preston Tuggle, Nick Turley, Jerry Tworek, Juan Felipe Cerón Uribe, Andrea Vallone, Arun Vijayvergiya, Chelsea Voss, Carroll Wainwright, Justin Jay Wang, Alvin Wang, Ben Wang, Jonathan Ward, Jason Wei, CJ Weinmann, Akila Welihinda, Peter Welinder, Jiayi Weng, Lilian Weng, Matt Wiethoff, Dave Willner, Clemens Winter, Samuel Wolrich, Hannah Wong, Lauren Workman, Sherwin Wu, Jeff Wu, Michael Wu, Kai Xiao, Tao Xu, Sarah Yoo, Kevin Yu, Qiming Yuan, Wojciech Zaremba, Rowan Zellers, Chong Zhang, Marvin Zhang, Shengjia Zhao, Tianhao Zheng, Juntang Zhuang, William Zhuk, Barret Zoph

We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs.

Arithmetic Reasoning Bug fixing +7

Data Selection for Language Models via Importance Resampling

1 code implementation NeurIPS 2023 Sang Michael Xie, Shibani Santurkar, Tengyu Ma, Percy Liang

To measure whether hashed n-gram features preserve the aspects of the data that are relevant to the target, we define KL reduction, a data metric that measures the proximity between the selected pretraining data and the target on some feature space.

Is a Caption Worth a Thousand Images? A Controlled Study for Representation Learning

no code implementations15 Jul 2022 Shibani Santurkar, Yann Dubois, Rohan Taori, Percy Liang, Tatsunori Hashimoto

The development of CLIP [Radford et al., 2021] has sparked a debate on whether language supervision can result in vision models with more transferable representations than traditional image-only methods.

Descriptive Representation Learning

Editing a classifier by rewriting its prediction rules

1 code implementation NeurIPS 2021 Shibani Santurkar, Dimitris Tsipras, Mahalaxmi Elango, David Bau, Antonio Torralba, Aleksander Madry

We present a methodology for modifying the behavior of a classifier by directly rewriting its prediction rules.

3DB: A Framework for Debugging Computer Vision Models

1 code implementation7 Jun 2021 Guillaume Leclerc, Hadi Salman, Andrew Ilyas, Sai Vemprala, Logan Engstrom, Vibhav Vineet, Kai Xiao, Pengchuan Zhang, Shibani Santurkar, Greg Yang, Ashish Kapoor, Aleksander Madry

We introduce 3DB: an extendable, unified framework for testing and debugging vision models using photorealistic simulation.

Leveraging Sparse Linear Layers for Debuggable Deep Networks

2 code implementations11 May 2021 Eric Wong, Shibani Santurkar, Aleksander Mądry

We show how fitting sparse linear models over learned deep feature representations can lead to more debuggable neural networks.

BREEDS: Benchmarks for Subpopulation Shift

2 code implementations ICLR 2021 Shibani Santurkar, Dimitris Tsipras, Aleksander Madry

We develop a methodology for assessing the robustness of models to subpopulation shift---specifically, their ability to generalize to novel data subpopulations that were not observed during training.

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

2 code implementations25 May 2020 Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Firdaus Janoos, Larry Rudolph, Aleksander Madry

We study the roots of algorithmic progress in deep policy gradient algorithms through a case study on two popular algorithms: Proximal Policy Optimization (PPO) and Trust Region Policy Optimization (TRPO).

reinforcement-learning Reinforcement Learning (RL)

Identifying Statistical Bias in Dataset Replication

1 code implementation19 May 2020 Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Jacob Steinhardt, Aleksander Madry

We study ImageNet-v2, a replication of the ImageNet dataset on which models exhibit a significant (11-14%) drop in accuracy, even after controlling for a standard human-in-the-loop measure of data quality.

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

2 code implementations ICLR 2020 Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Firdaus Janoos, Larry Rudolph, Aleksander Madry

We study the roots of algorithmic progress in deep policy gradient algorithms through a case study on two popular algorithms, Proximal Policy Optimization and Trust Region Policy Optimization.

reinforcement-learning Reinforcement Learning (RL)

Image Synthesis with a Single (Robust) Classifier

1 code implementation NeurIPS 2019 Shibani Santurkar, Dimitris Tsipras, Brandon Tran, Andrew Ilyas, Logan Engstrom, Aleksander Madry

We show that the basic classification framework alone can be used to tackle some of the most challenging tasks in image synthesis.

Ranked #60 on Image Generation on CIFAR-10 (Inception score metric)

Adversarial Robustness Image Generation

Adversarial Robustness as a Prior for Learned Representations

5 code implementations3 Jun 2019 Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Brandon Tran, Aleksander Madry

In this work, we show that robust optimization can be re-cast as a tool for enforcing priors on the features learned by deep neural networks.

Adversarial Robustness

Adversarial Examples Are Not Bugs, They Are Features

4 code implementations NeurIPS 2019 Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, Aleksander Madry

Adversarial examples have attracted significant attention in machine learning, but the reasons for their existence and pervasiveness remain unclear.

BIG-bench Machine Learning

A Closer Look at Deep Policy Gradients

no code implementations ICLR 2020 Andrew Ilyas, Logan Engstrom, Shibani Santurkar, Dimitris Tsipras, Firdaus Janoos, Larry Rudolph, Aleksander Madry

We study how the behavior of deep policy gradient algorithms reflects the conceptual framework motivating their development.

Value prediction

Robustness May Be at Odds with Accuracy

7 code implementations ICLR 2019 Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, Aleksander Madry

We show that there may exist an inherent tension between the goal of adversarial robustness and that of standard generalization.

Adversarial Robustness

How Does Batch Normalization Help Optimization?

11 code implementations NeurIPS 2018 Shibani Santurkar, Dimitris Tsipras, Andrew Ilyas, Aleksander Madry

Batch Normalization (BatchNorm) is a widely adopted technique that enables faster and more stable training of deep neural networks (DNNs).

Adversarially Robust Generalization Requires More Data

no code implementations NeurIPS 2018 Ludwig Schmidt, Shibani Santurkar, Dimitris Tsipras, Kunal Talwar, Aleksander Mądry

We postulate that the difficulty of training robust classifiers stems, at least partially, from this inherently larger sample complexity.

General Classification Image Classification

A Classification-Based Perspective on GAN Distributions

no code implementations ICLR 2018 Shibani Santurkar, Ludwig Schmidt, Aleksander Madry

A fundamental, and still largely unanswered, question in the context of Generative Adversarial Networks (GANs) is whether GANs are actually able to capture the key characteristics of the datasets they are trained on.

Classification General Classification

A Classification-Based Study of Covariate Shift in GAN Distributions

no code implementations ICML 2018 Shibani Santurkar, Ludwig Schmidt, Aleksander Mądry

A basic, and still largely unanswered, question in the context of Generative Adversarial Networks (GANs) is whether they are truly able to capture all the fundamental characteristics of the distributions they are trained on.

Classification General Classification

Generative Compression

no code implementations4 Mar 2017 Shibani Santurkar, David Budden, Nir Shavit

Traditional image and video compression algorithms rely on hand-crafted encoder/decoder pairs (codecs) that lack adaptability and are agnostic to the data being compressed.

Video Compression

Toward Streaming Synapse Detection with Compositional ConvNets

no code implementations23 Feb 2017 Shibani Santurkar, David Budden, Alexander Matveev, Heather Berlin, Hayk Saribekyan, Yaron Meirovitch, Nir Shavit

Connectomics is an emerging field in neuroscience that aims to reconstruct the 3-dimensional morphology of neurons from electron microscopy (EM) images.

Deep Tensor Convolution on Multicores

no code implementations ICML 2017 David Budden, Alexander Matveev, Shibani Santurkar, Shraman Ray Chaudhuri, Nir Shavit

Deep convolutional neural networks (ConvNets) of 3-dimensional kernels allow joint modeling of spatiotemporal features.

A neural circuit for navigation inspired by C. elegans Chemotaxis

no code implementations29 Oct 2014 Shibani Santurkar, Bipin Rajendran

In order to harness the computational advantages spiking neural networks promise over their non-spiking counterparts, we develop a network comprising 7-spiking neurons with non-plastic synapses which we show is extremely robust in tracking a range of concentrations.

Sub-threshold CMOS Spiking Neuron Circuit Design for Navigation Inspired by C. elegans Chemotaxis

no code implementations29 Oct 2014 Shibani Santurkar, Bipin Rajendran

We demonstrate a spiking neural network for navigation motivated by the chemotaxis network of Caenorhabditis elegans.

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