97 citations · 184 across the 26 of their papers we have counts for
7 papers · 1 filter
Wave Physics-informed Matrix Factorizations
Harsha Vardhan Tetali, Joel B. Harley, Benjamin D. Haeffele
With the recent success of representation learning methods, which includes deep learning as a special case, there has been considerable interest in developing techniques that incor…
White-Box Transformers via Sparse Rate Reduction: Compression Is All There Is?
Yaodong Yu, Sam Buchanan, Druv Pai +7
In this paper, we contend that a natural objective of representation learning is to compress and transform the distribution of the data, say sets of tokens, towards a low-dimension…
Variational Information Pursuit with Large Language and Multimodal Models for Interpretable Predictions
Kwan Ho Ryan Chan, Aditya Chattopadhyay, Benjamin David Haeffele +1
Variational Information Pursuit (V-IP) is a framework for making interpretable predictions by design by sequentially selecting a short chain of task-relevant, user-defined and inte…
Image Clustering via the Principle of Rate Reduction in the Age of Pretrained Models
Tianzhe Chu, Shengbang Tong, Tianjiao Ding +4
The advent of large pre-trained models has brought about a paradigm shift in both visual representation learning and natural language processing. However, clustering unlabeled imag…
White-Box Transformers via Sparse Rate Reduction
Yaodong Yu, Sam Buchanan, Druv Pai +5
In this paper, we contend that the objective of representation learning is to compress and transform the distribution of the data, say sets of tokens, towards a mixture of low-dime…
Variational Information Pursuit for Interpretable Predictions
Aditya Chattopadhyay, Kwan Ho Ryan Chan, Benjamin D. Haeffele +2
There is a growing interest in the machine learning community in developing predictive algorithms that are "interpretable by design". Towards this end, recent work proposes to make…