1 citations · 1 across the 2 of their papers we have counts for
4 papers
Low-Precision Reinforcement Learning: Running Soft Actor-Critic in Half Precision
Johan Bjorck, Xiangyu Chen, Christopher De Sa +2
Low-precision training has become a popular approach to reduce compute requirements, memory footprint, and energy consumption in supervised learning. In contrast, this promising ap…
Understanding Decoupled and Early Weight Decay
Johan Bjorck, Kilian Weinberger, Carla Gomes
Weight decay (WD) is a traditional regularization technique in deep learning, but despite its ubiquity, its behavior is still an area of active research. Golatkar et al. have recen…
Automatic Detection and Compression for Passive Acoustic Monitoring of the African Forest Elephant
Johan Bjorck, Brendan H. Rappazzo, Di Chen +3
In this work, we consider applying machine learning to the analysis and compression of audio signals in the context of monitoring elephants in sub-Saharan Africa. Earth's biodivers…
Understanding Batch Normalization
Johan Bjorck, Carla Gomes, Bart Selman +1
Batch normalization (BN) is a technique to normalize activations in intermediate layers of deep neural networks. Its tendency to improve accuracy and speed up training have establi…