research
∙
06/23/2023
Variance-Covariance Regularization Improves Representation Learning
Transfer learning has emerged as a key approach in the machine learning ...
share
research
∙
05/24/2023
Reverse Engineering Self-Supervised Learning
Self-supervised learning (SSL) is a powerful tool in machine learning, b...
research
∙
04/19/2023
To Compress or Not to Compress- Self-Supervised Learning and Information Theory: A Review
Deep neural networks have demonstrated remarkable performance in supervi...
research
∙
03/01/2023
An Information-Theoretic Perspective on Variance-Invariance-Covariance Regularization
In this paper, we provide an information-theoretic perspective on Varian...
research
∙
10/12/2022
How Much Data Are Augmentations Worth? An Investigation into Scaling Laws, Invariance, and Implicit Regularization
Despite the clear performance benefits of data augmentations, little is ...
research
∙
07/20/2022
What Do We Maximize in Self-Supervised Learning?
In this paper, we examine self-supervised learning methods, particularly...
research
∙
05/20/2022
Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors
Deep learning is increasingly moving towards a transfer learning paradig...
research
∙
02/10/2022
Information Flow in Deep Neural Networks
Although deep neural networks have been immensely successful, there is n...
research
∙
06/06/2021
Tabular Data: Deep Learning is Not All You Need
A key element of AutoML systems is setting the types of models that will...
research
∙
06/08/2020
The Dual Information Bottleneck
The Information Bottleneck (IB) framework is a general characterization ...
research
∙
11/26/2018
