3 citations · 5 across the 6 of their papers we have counts for
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cs.LG2023
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…
cs.CV2023★ 3 cited
Emergence of Segmentation with Minimalistic White-Box Transformers
Yaodong Yu, Tianzhe Chu, Shengbang Tong +4
Transformer-like models for vision tasks have recently proven effective for a wide range of downstream applications such as segmentation and detection. Previous works have shown th…
cs.CV2023
Canonical Factors for Hybrid Neural Fields
Brent Yi, Weijia Zeng, Sam Buchanan +1
Factored feature volumes offer a simple way to build more compact, efficient, and intepretable neural fields, but also introduce biases that are not necessarily beneficial for real…