Get our free extension to see links to code for papers anywhere online!

Add to Chrome

Add to Firefox

Get Pro 💎 Log In/Sign Up 🚀

Picture for Avi Schwarzschild

Avi Schwarzschild

https://www.catalyzex.com

A Cookbook of Self-Supervised Learning

Apr 24, 2023
Randall Balestriero, Mark Ibrahim, Vlad Sobal, Ari Morcos, Shashank Shekhar, Tom Goldstein, Florian Bordes, Adrien Bardes, Gregoire Mialon, Yuandong Tian, Avi Schwarzschild, Andrew Gordon Wilson, Jonas Geiping, Quentin Garrido, Pierre Fernandez, Amir Bar, Hamed Pirsiavash, Yann LeCun, Micah Goldblum

Figure 1 for A Cookbook of Self-Supervised Learning

Figure 2 for A Cookbook of Self-Supervised Learning

Figure 3 for A Cookbook of Self-Supervised Learning

Figure 4 for A Cookbook of Self-Supervised Learning

Access Paper or Ask Questions

Reckoning with the Disagreement Problem: Explanation Consensus as a Training Objective

Mar 23, 2023
Avi Schwarzschild, Max Cembalest, Karthik Rao, Keegan Hines, John Dickerson

Figure 1 for Reckoning with the Disagreement Problem: Explanation Consensus as a Training Objective

Figure 2 for Reckoning with the Disagreement Problem: Explanation Consensus as a Training Objective

Figure 3 for Reckoning with the Disagreement Problem: Explanation Consensus as a Training Objective

Figure 4 for Reckoning with the Disagreement Problem: Explanation Consensus as a Training Objective

Access Paper or Ask Questions

Neural Auctions Compromise Bidder Information

Feb 28, 2023
Alex Stein, Avi Schwarzschild, Michael Curry, Tom Goldstein, John Dickerson

Figure 1 for Neural Auctions Compromise Bidder Information

Figure 2 for Neural Auctions Compromise Bidder Information

Figure 3 for Neural Auctions Compromise Bidder Information

Figure 4 for Neural Auctions Compromise Bidder Information

Access Paper or Ask Questions

Universal Guidance for Diffusion Models

Feb 14, 2023
Arpit Bansal, Hong-Min Chu, Avi Schwarzschild, Soumyadip Sengupta, Micah Goldblum, Jonas Geiping, Tom Goldstein

Figure 1 for Universal Guidance for Diffusion Models

Figure 2 for Universal Guidance for Diffusion Models

Figure 3 for Universal Guidance for Diffusion Models

Figure 4 for Universal Guidance for Diffusion Models

Access Paper or Ask Questions

Transfer Learning with Deep Tabular Models

Jun 30, 2022
Roman Levin, Valeriia Cherepanova, Avi Schwarzschild, Arpit Bansal, C. Bayan Bruss, Tom Goldstein, Andrew Gordon Wilson, Micah Goldblum

Figure 1 for Transfer Learning with Deep Tabular Models

Figure 2 for Transfer Learning with Deep Tabular Models

Figure 3 for Transfer Learning with Deep Tabular Models

Figure 4 for Transfer Learning with Deep Tabular Models

Access Paper or Ask Questions

End-to-end Algorithm Synthesis with Recurrent Networks: Logical Extrapolation Without Overthinking

Feb 15, 2022
Arpit Bansal, Avi Schwarzschild, Eitan Borgnia, Zeyad Emam, Furong Huang, Micah Goldblum, Tom Goldstein

Figure 1 for End-to-end Algorithm Synthesis with Recurrent Networks: Logical Extrapolation Without Overthinking

Figure 2 for End-to-end Algorithm Synthesis with Recurrent Networks: Logical Extrapolation Without Overthinking

Figure 3 for End-to-end Algorithm Synthesis with Recurrent Networks: Logical Extrapolation Without Overthinking

Figure 4 for End-to-end Algorithm Synthesis with Recurrent Networks: Logical Extrapolation Without Overthinking

Access Paper or Ask Questions

Datasets for Studying Generalization from Easy to Hard Examples

Aug 13, 2021
Avi Schwarzschild, Eitan Borgnia, Arjun Gupta, Arpit Bansal, Zeyad Emam, Furong Huang, Micah Goldblum, Tom Goldstein

Figure 1 for Datasets for Studying Generalization from Easy to Hard Examples

Figure 2 for Datasets for Studying Generalization from Easy to Hard Examples

Figure 3 for Datasets for Studying Generalization from Easy to Hard Examples

Access Paper or Ask Questions

MetaBalance: High-Performance Neural Networks for Class-Imbalanced Data

Jun 17, 2021
Arpit Bansal, Micah Goldblum, Valeriia Cherepanova, Avi Schwarzschild, C. Bayan Bruss, Tom Goldstein

Figure 1 for MetaBalance: High-Performance Neural Networks for Class-Imbalanced Data

Figure 2 for MetaBalance: High-Performance Neural Networks for Class-Imbalanced Data

Figure 3 for MetaBalance: High-Performance Neural Networks for Class-Imbalanced Data

Figure 4 for MetaBalance: High-Performance Neural Networks for Class-Imbalanced Data

Access Paper or Ask Questions

Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks

Jun 08, 2021
Avi Schwarzschild, Eitan Borgnia, Arjun Gupta, Furong Huang, Uzi Vishkin, Micah Goldblum, Tom Goldstein

Figure 1 for Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks

Figure 2 for Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks

Figure 3 for Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks

Figure 4 for Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks

Access Paper or Ask Questions

SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training

Jun 02, 2021
Gowthami Somepalli, Micah Goldblum, Avi Schwarzschild, C. Bayan Bruss, Tom Goldstein

Figure 1 for SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training

Figure 2 for SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training

Figure 3 for SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training

Figure 4 for SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training

Access Paper or Ask Questions

Log in or sign up instantly 🚀

🔥 Discover incredible developments in machine intelligence

🤖 Quickly get code for any ML model

🔑 Get help from authors, engineers & researchers

Continue with email

Continue with Google

Continue with Github

Continue with LinkedIn

Continue with Facebook

Continue with Twitter

Seconds away from lift-off 🚀

To ensure authors get your request, sign in to proceed instantly

Continue with email

Continue with Github

Continue with Twitter

Continue with Google

Continue with LinkedIn

Continue with Facebook

© 2023 CatalyzeX

Follow us