13 citations · 15 across the 9 of their papers we have counts for
3 papers · 1 filter
TATAA: Programmable Mixed-Precision Transformer Acceleration with a Transformable Arithmetic Architecture
Jiajun Wu, Mo Song, Jingmin Zhao +3
Modern transformer-based deep neural networks present unique technical challenges for effective acceleration in real-world applications. Apart from the vast amount of linear operat…
Range, not Independence, Drives Modularity in Biologically Inspired Representations
Will Dorrell, Kyle Hsu, Luke Hollingsworth +6
Why do biological and artificial neurons sometimes modularise, each encoding a single meaningful variable, and sometimes entangle their representation of many variables? In this wo…
Tripod: Three Complementary Inductive Biases for Disentangled Representation Learning
Kyle Hsu, Jubayer Ibn Hamid, Kaylee Burns +2
Inductive biases are crucial in disentangled representation learning for narrowing down an underspecified solution set. In this work, we consider endowing a neural network autoenco…