Jeff Z. HaoChen | DeepAI

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∙ 06/28/2023

Beyond NTK with Vanilla Gradient Descent: A Mean-Field Analysis of Neural Networks with Polynomial Width, Samples, and Time

Despite recent theoretical progress on the non-convex optimization of tw...

0 Arvind Mahankali, et al. ∙

∙ 05/27/2023

Beyond Positive Scaling: How Negation Impacts Scaling Trends of Language Models

Language models have been shown to exhibit positive scaling, where perfo...

0 Yuhui Zhang, et al. ∙

∙ 02/08/2023

Diagnosing and Rectifying Vision Models using Language

Recent multi-modal contrastive learning models have demonstrated the abi...

0 Yuhui Zhang, et al. ∙

∙ 11/27/2022

A Theoretical Study of Inductive Biases in Contrastive Learning

Understanding self-supervised learning is important but challenging. Pre...

0 Jeff Z. HaoChen, et al. ∙

∙ 04/06/2022

Beyond Separability: Analyzing the Linear Transferability of Contrastive Representations to Related Subpopulations

Contrastive learning is a highly effective method which uses unlabeled d...

0 Jeff Z. HaoChen, et al. ∙

∙ 04/01/2022

Connect, Not Collapse: Explaining Contrastive Learning for Unsupervised Domain Adaptation

We consider unsupervised domain adaptation (UDA), where labeled data fro...

6 Kendrick Shen, et al. ∙

∙ 02/28/2022

Amortized Proximal Optimization

We propose a framework for online meta-optimization of parameters that g...

10 Juhan Bae, et al. ∙

∙ 10/11/2021

Self-supervised Learning is More Robust to Dataset Imbalance

Self-supervised learning (SSL) is a scalable way to learn general visual...

4 Hong Liu, et al. ∙

∙ 06/08/2021

Provable Guarantees for Self-Supervised Deep Learning with Spectral Contrastive Loss

Recent works in self-supervised learning have advanced the state-of-the-...

2 Jeff Z. HaoChen, et al. ∙

∙ 11/03/2020

Meta-learning Transferable Representations with a Single Target Domain

Recent works found that fine-tuning and joint training—two popular appro...

0 Hong Liu, et al. ∙

∙ 06/15/2020

Shape Matters: Understanding the Implicit Bias of the Noise Covariance

The noise in stochastic gradient descent (SGD) provides a crucial implic...

9 Jeff Z. HaoChen, et al. ∙

∙ 06/26/2018

Random Shuffling Beats SGD after Finite Epochs

A long-standing problem in the theory of stochastic gradient descent (SG...

0 Jeff Z. HaoChen, et al. ∙

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