49 citations · 408 across the 42 of their papers we have counts for
4 papers · 1 filter
Demystifying and Generalizing BinaryConnect
Tim Dockhorn, Yaoliang Yu, Eyyüb Sari +2
BinaryConnect (BC) and its many variations have become the de facto standard for neural network quantization. However, our understanding of the inner workings of BC is still quite…
An Operator Splitting View of Federated Learning
Saber Malekmohammadi, Kiarash Shaloudegi, Zeou Hu +1
Over the past few years, the federated learning () community has witnessed a proliferation of new algorithms. However, our understating of the theory of…
: Sign-Sparse-Shift Reparametrization for Effective Training of Low-bit Shift Networks
Xinlin Li, Bang Liu, Yaoliang Yu +3
Shift neural networks reduce computation complexity by removing expensive multiplication operations and quantizing continuous weights into low-bit discrete values, which are fast a…
Quantifying and Improving Transferability in Domain Generalization
Guojun Zhang, Han Zhao, Yaoliang Yu +1
Out-of-distribution generalization is one of the key challenges when transferring a model from the lab to the real world. Existing efforts mostly focus on building invariant featur…