Chiyuan Zhang | DeepAI

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Featured Co-authors

Zheng Zhang
185 publications
Zhou Yu
123 publications
Oriol Vinyals
99 publications
Lorenzo Rosasco
88 publications
Remi Munos
88 publications
Nicolas Papernot
83 publications
Jon Kleinberg
75 publications
Mu Li
72 publications
Alexander Rakhlin
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Ting Chen
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Nicholas Carlini
57 publications

∙ 10/31/2022

Preventing Verbatim Memorization in Language Models Gives a False Sense of Privacy

Studying data memorization in neural language models helps us understand...

0 Daphne Ippolito, et al. ∙

∙ 06/21/2022

The Privacy Onion Effect: Memorization is Relative

Machine learning models trained on private datasets have been shown to l...

0 Nicholas Carlini, et al. ∙

∙ 05/26/2022

Learning to Reason with Neural Networks: Generalization, Unseen Data and Boolean Measures

This paper considers the Pointer Value Retrieval (PVR) benchmark introdu...

90 Emmanuel Abbe, et al. ∙

∙ 04/15/2022

Just Fine-tune Twice: Selective Differential Privacy for Large Language Models

With the increasing adoption of NLP models in real-world products, it be...

0 Weiyan Shi, et al. ∙

∙ 02/15/2022

Quantifying Memorization Across Neural Language Models

Large language models (LMs) have been shown to memorize parts of their t...

1 Nicholas Carlini, et al. ∙

∙ 12/24/2021

Counterfactual Memorization in Neural Language Models

Modern neural language models widely used in tasks across NLP risk memor...

25 Chiyuan Zhang, et al. ∙

∙ 10/15/2021

Understanding and Improving Robustness of Vision Transformers through Patch-based Negative Augmentation

We investigate the robustness of vision transformers (ViTs) through the ...

27 Yao Qin, et al. ∙

∙ 08/19/2021

Do Vision Transformers See Like Convolutional Neural Networks?

Convolutional neural networks (CNNs) have so far been the de-facto model...

33 Maithra Raghu, et al. ∙

∙ 07/27/2021

Pointer Value Retrieval: A new benchmark for understanding the limits of neural network generalization

The successes of deep learning critically rely on the ability of neural ...

16 Chiyuan Zhang, et al. ∙

∙ 07/14/2021

Deduplicating Training Data Makes Language Models Better

We find that existing language modeling datasets contain many near-dupli...

0 Katherine Lee, et al. ∙

∙ 03/15/2021

Understanding invariance via feedforward inversion of discriminatively trained classifiers

A discriminatively trained neural net classifier achieves optimal perfor...

28 Piotr Teterwak, et al. ∙

∙ 02/11/2021

On Deep Learning with Label Differential Privacy

In many machine learning applications, the training data can contain hig...

0 Badih Ghazi, et al. ∙

∙ 08/26/2020

What is being transferred in transfer learning?

One desired capability for machines is the ability to transfer their kno...

78 Behnam Neyshabur, et al. ∙

∙ 08/09/2020

What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation

Deep learning algorithms are well-known to have a propensity for fitting...

64 Vitaly Feldman, et al. ∙

∙ 02/08/2020

Exploring the Memorization-Generalization Continuum in Deep Learning

Human learners appreciate that some facts demand memorization whereas ot...

9 Ziheng Jiang, et al. ∙

∙ 02/14/2019

Transfusion: Understanding Transfer Learning with Applications to Medical Imaging

With the increasingly varied applications of deep learning, transfer lea...

26 Maithra Raghu, et al. ∙

∙ 02/13/2019

Identity Crisis: Memorization and Generalization under Extreme Overparameterization

We study the interplay between memorization and generalization of overpa...

26 Chiyuan Zhang, et al. ∙

∙ 02/06/2019

Are All Layers Created Equal?

Understanding learning and generalization of deep architectures has been...

0 Chiyuan Zhang, et al. ∙

∙ 09/22/2018

Unrestricted Adversarial Examples

We introduce a two-player contest for evaluating the safety and robustne...

14 Tom B. Brown, et al. ∙

∙ 04/18/2018

A Study on Overfitting in Deep Reinforcement Learning

Recent years have witnessed significant progresses in deep Reinforcement...

0 Chiyuan Zhang, et al. ∙

∙ 02/21/2018

Machine Theory of Mind

Theory of mind (ToM; Premack & Woodruff, 1978) broadly refers to humans'...

0 Neil C. Rabinowitz, et al. ∙

∙ 01/07/2018

Theory of Deep Learning IIb: Optimization Properties of SGD

In Theory IIb we characterize with a mix of theory and experiments the o...

0 Chiyuan Zhang, et al. ∙

∙ 12/03/2015

MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems

MXNet is a multi-language machine learning (ML) library to ease the deve...

0 Tianqi Chen, et al. ∙

∙ 06/17/2015

Learning with a Wasserstein Loss

Learning to predict multi-label outputs is challenging, but in many prob...

0 Charlie Frogner, et al. ∙

∙ 04/01/2014

A Deep Representation for Invariance And Music Classification

Representations in the auditory cortex might be based on mechanisms simi...

0 Chiyuan Zhang, et al. ∙