23 citations · 41 across the 11 of their papers we have counts for
5 papers · 1 filter
Principles and Practice of Deep Representation Learning: or a Mathematical Theory of Memory
Sam Buchanan, Druv Pai, Peng Wang +1
In the current era of deep learning and especially generative models, there is significant investment in training very large deep neural networks. Thus far, such models have been "…
On the Edge of Memorization in Diffusion Models
Sam Buchanan, Druv Pai, Yi Ma +1
When do diffusion models reproduce their training data, and when are they able to generate samples beyond it? A practically relevant theoretical understanding of this interplay bet…
Masked Completion via Structured Diffusion with White-Box Transformers
Druv Pai, Ziyang Wu, Sam Buchanan +2
Modern learning frameworks often train deep neural networks with massive amounts of unlabeled data to learn representations by solving simple pretext tasks, then use the representa…
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…
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…