3 citations · 6 across the 5 of their papers we have counts for
5 papers
Accuracy on the wrong line: On the pitfalls of noisy data for out-of-distribution generalisation
Amartya Sanyal, Yaxi Hu, Yaodong Yu +3
"Accuracy-on-the-line" is a widely observed phenomenon in machine learning, where a model's accuracy on in-distribution (ID) and out-of-distribution (OOD) data is positively correl…
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…
Differentially Private Representation Learning via Image Captioning
Tom Sander, Yaodong Yu, Maziar Sanjabi +4
Differentially private (DP) machine learning is considered the gold-standard solution for training a model from sensitive data while still preserving privacy. However, a major barr…