7 citations · 7 across the 5 of their papers we have counts for
5 papers
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…
Attention-Only Transformers via Unrolled Subspace Denoising
Peng Wang, Yifu Lu, Yaodong Yu +3
Despite the popularity of transformers in practice, their architectures are empirically designed and neither mathematically justified nor interpretable. Moreover, as indicated by m…
Simplifying DINO via Coding Rate Regularization
Ziyang Wu, Jingyuan Zhang, Druv Pai +5
DINO and DINOv2 are two model families being widely used to learn representations from unlabeled imagery data at large scales. Their learned representations often enable state-of-t…
Token Statistics Transformer: Linear-Time Attention via Variational Rate Reduction
Ziyang Wu, Tianjiao Ding, Yifu Lu +6
The attention operator is arguably the key distinguishing factor of transformer architectures, which have demonstrated state-of-the-art performance on a variety of tasks. However,…
Active-Dormant Attention Heads: Mechanistically Demystifying Extreme-Token Phenomena in LLMs
Tianyu Guo, Druv Pai, Yu Bai +3
Practitioners have consistently observed three puzzling phenomena in transformer-based large language models (LLMs): attention sinks, value-state drains, and residual-state peaks,…