Avi Schwarzschild | DeepAI

DeepAI AI Chat

Featured Co-authors

Bo Li
461 publications
Tom Goldstein
140 publications
Dawn Song
130 publications
Yann LeCun
125 publications
Andrew Gordon Wilson
92 publications
Christoph Studer
76 publications
Yuandong Tian
69 publications
Micah Goldblum
68 publications
Furong Huang
62 publications
John P Dickerson
62 publications
Randall Balestriero
55 publications

∙ 09/01/2023

Baseline Defenses for Adversarial Attacks Against Aligned Language Models

As Large Language Models quickly become ubiquitous, it becomes critical ...

0 Neel Jain, et al. ∙

∙ 04/24/2023

A Cookbook of Self-Supervised Learning

Self-supervised learning, dubbed the dark matter of intelligence, is a p...

0 Randall Balestriero, et al. ∙

∙ 03/23/2023

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

As neural networks increasingly make critical decisions in high-stakes s...

2 Avi Schwarzschild, et al. ∙

∙ 02/28/2023

Neural Auctions Compromise Bidder Information

Single-shot auctions are commonly used as a means to sell goods, for exa...

0 Alex Stein, et al. ∙

∙ 02/14/2023

Universal Guidance for Diffusion Models

Typical diffusion models are trained to accept a particular form of cond...

0 Arpit Bansal, et al. ∙

∙ 02/11/2022

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

Machine learning systems perform well on pattern matching tasks, but the...

4 Arpit Bansal, et al. ∙

∙ 08/13/2021

Datasets for Studying Generalization from Easy to Hard Examples

We describe new datasets for studying generalization from easy to hard e...

0 Avi Schwarzschild, et al. ∙

∙ 06/17/2021

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

Class-imbalanced data, in which some classes contain far more samples th...

0 Arpit Bansal, et al. ∙

∙ 06/08/2021

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

Deep neural networks are powerful machines for visual pattern recognitio...

23 Avi Schwarzschild, et al. ∙

∙ 06/02/2021

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

Tabular data underpins numerous high-impact applications of machine lear...

1 Gowthami Somepalli, et al. ∙

∙ 02/22/2021

Thinking Deeply with Recurrence: Generalizing from Easy to Hard Sequential Reasoning Problems

Deep neural networks are powerful machines for visual pattern recognitio...

7 Avi Schwarzschild, et al. ∙

∙ 12/18/2020

Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses

As machine learning systems grow in scale, so do their training data req...

71 Micah Goldblum, et al. ∙

∙ 06/22/2020

Just How Toxic is Data Poisoning? A Unified Benchmark for Backdoor and Data Poisoning Attacks

Data poisoning and backdoor attacks manipulate training data in order to...

0 Avi Schwarzschild, et al. ∙

∙ 04/20/2020

Headless Horseman: Adversarial Attacks on Transfer Learning Models

Transfer learning facilitates the training of task-specific classifiers ...

0 Ahmed Abdelkader, et al. ∙

∙ 02/21/2020

Adversarial Attacks on Machine Learning Systems for High-Frequency Trading

Algorithmic trading systems are often completely automated, and deep lea...

0 Micah Goldblum, et al. ∙

∙ 10/01/2019

Truth or Backpropaganda? An Empirical Investigation of Deep Learning Theory

We empirically evaluate common assumptions about neural networks that ar...

0 Micah Goldblum, et al. ∙

Are you a researcher?
Expose your work
to one of the largest
A.I. communities in the world