2 citations · 3 across the 5 of their papers we have counts for
5 papers
The price of multi-group transductive learning
Noah Bergam, Samuel Deng, Daniel Hsu
We show every multi-group learner in the leave-one-out transductive setting may incur a surprisingly large multiplicative penalty in its error rate on some group relative to the er…
Fixed Universal Transformers
Jingwen Liu, Alexandr Andoni, Daniel Hsu
We introduce \emph{universal transformers}: fixed transformers that can simulate any transformer in a given class via a suitable input embedding. Analogous to a universal Turing ma…
Fast attention mechanisms: a tale of parallelism
Jingwen Liu, Hantao Yu, Clayton Sanford +2
Transformers have the representational capacity to simulate Massively Parallel Computation (MPC) algorithms, but they suffer from quadratic time complexity, which severely limits t…
One-layer transformers fail to solve the induction heads task
Clayton Sanford, Daniel Hsu, Matus Telgarsky
A simple communication complexity argument proves that no one-layer transformer can solve the induction heads task unless its size is exponentially larger than the size sufficient…
Transformers, parallel computation, and logarithmic depth
Clayton Sanford, Daniel Hsu, Matus Telgarsky
We show that a constant number of self-attention layers can efficiently simulate, and be simulated by, a constant number of communication rounds of Massively Parallel Computation.…