2 citations · 2 across the 5 of their papers we have counts for
3 papers · 1 filter
Free Random Projection for In-Context Reinforcement Learning
Tomohiro Hayase, Benoît Collins, Nakamasa Inoue
Hierarchical inductive biases are hypothesized to promote generalizable policies in reinforcement learning, as demonstrated by explicit hyperbolic latent representations and archit…
Understanding MLP-Mixer as a Wide and Sparse MLP
Tomohiro Hayase, Ryo Karakida
Multi-layer perceptron (MLP) is a fundamental component of deep learning, and recent MLP-based architectures, especially the MLP-Mixer, have achieved significant empirical success.…
Layer-Wise Interpretation of Deep Neural Networks Using Identity Initialization
Shohei Kubota, Hideaki Hayashi, Tomohiro Hayase +1
The interpretability of neural networks (NNs) is a challenging but essential topic for transparency in the decision-making process using machine learning. One of the reasons for th…