3 papers
cs.NE2026
Frame Theoretical Derivation of Three Factor Learning Rule for Oja's Subspace Rule
Taiki Yamada
We show that the error-gated Hebbian rule for PCA (EGHR-PCA), a three-factor learning rule equivalent to Oja's subspace rule under Gaussian inputs, can be systematically derived fr…
math.CO2025
Vertex evaluation of multiplex graphs using Forman Curvature
Taiki Yamada
The identification of vertices that play a central role in network analysis is a fundamental challenge. Although traditional centrality measures have been extensively employed for…
cs.LG2025
Unsupervised Learning in Echo State Networks for Input Reconstruction
Taiki Yamada, Yuichi Katori, Kantaro Fujiwara
Echo state networks (ESNs) are a class of recurrent neural networks in which only the readout layer is trainable, while the recurrent and input layers are fixed. This architectural…