25 citations · 59 across the 15 of their papers we have counts for
4 papers · 1 filter
C-SENN: Contrastive Self-Explaining Neural Network
Yoshihide Sawada, Keigo Nakamura
In this study, we use a self-explaining neural network (SENN), which learns unsupervised concepts, to acquire concepts that are easy for people to understand automatically. In conc…
Rethinking the role of normalization and residual blocks for spiking neural networks
Shin-ichi Ikegawa, Ryuji Saiin, Yoshihide Sawada +1
Biologically inspired spiking neural networks (SNNs) are widely used to realize ultralow-power energy consumption. However, deep SNNs are not easy to train due to the excessive fir…
Concept Bottleneck Model with Additional Unsupervised Concepts
Yoshihide Sawada, Keigo Nakamura
With the increasing demands for accountability, interpretability is becoming an essential capability for real-world AI applications. However, most methods utilize post-hoc approach…
SNN: Time Step Reduction of Spiking Surrogate Gradients for Training Energy Efficient Single-Step Spiking Neural Networks
Kazuma Suetake, Shin-ichi Ikegawa, Ryuji Saiin +1
As the scales of neural networks increase, techniques that enable them to run with low computational cost and energy efficiency are required. From such demands, various efficient n…