36 citations · 40 across the 2 of their papers we have counts for
2 papers
cs.CV2021★ 4 cited
Learnable Companding Quantization for Accurate Low-bit Neural Networks
Kohei Yamamoto
Quantizing deep neural networks is an effective method for reducing memory consumption and improving inference speed, and is thus useful for implementation in resource-constrained…
stat.ML2018★ 36 cited
PCAS: Pruning Channels with Attention Statistics for Deep Network Compression
Kohei Yamamoto, Kurato Maeno
Compression techniques for deep neural networks are important for implementing them on small embedded devices. In particular, channel-pruning is a useful technique for realizing co…