23 citations · 48 across the 16 of their papers we have counts for
6 papers · 1 filter
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,…
A Global Geometric Analysis of Maximal Coding Rate Reduction
Peng Wang, Huikang Liu, Druv Pai +4
The maximal coding rate reduction (MCR) objective for learning structured and compact deep representations is drawing increasing attention, especially after its recent usage in…
Scaling White-Box Transformers for Vision
Jinrui Yang, Xianhang Li, Druv Pai +4
CRATE, a white-box transformer architecture designed to learn compressed and sparse representations, offers an intriguing alternative to standard vision transformers (ViTs) due to…
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…
Congestion Pricing for Efficiency and Equity: Theory and Applications to the San Francisco Bay Area
Chinmay Maheshwari, Kshitij Kulkarni, Druv Pai +3
Congestion pricing, while adopted by many cities to alleviate traffic congestion, raises concerns about widening socioeconomic disparities due to its disproportionate impact on low…