67 citations · 114 across the 3 of their papers we have counts for
3 papers
cs.LG2021★ 26 cited
How Do Adam and Training Strategies Help BNNs Optimization?
Zechun Liu, Zhiqiang Shen, Shichao Li +3
The best performing Binary Neural Networks (BNNs) are usually attained using Adam optimization and its multi-step training variants. However, to the best of our knowledge, few stud…
cs.LG2020★ 21 cited
Larq Compute Engine: Design, Benchmark, and Deploy State-of-the-Art Binarized Neural Networks
Tom Bannink, Arash Bakhtiari, Adam Hillier +5
We introduce Larq Compute Engine, the world's fastest Binarized Neural Network (BNN) inference engine, and use this framework to investigate several important questions about the e…
cs.LG2019★ 67 cited
Latent Weights Do Not Exist: Rethinking Binarized Neural Network Optimization
Koen Helwegen, James Widdicombe, Lukas Geiger +3
Optimization of Binarized Neural Networks (BNNs) currently relies on real-valued latent weights to accumulate small update steps. In this paper, we argue that these latent weights…