3 papers
stat.ML2026
Multiscale Euclidean Network Trajectories: Second-Moment Geometry, Attribution, and Change Points
Haruka Ezoe, Ryohei Hisano
A central challenge in dynamic network analysis is to represent temporal evolution in a way that is both geometrically meaningful and statistically identifiable. One approach embed…
stat.ML2026
Unfolded Laplacian Spectral Embedding: A Theoretically Grounded Approach to Dynamic Network Representation
Haruka Ezoe, Hiroki Matsumoto, Ryohei Hisano
Dynamic relational data arise in many machine learning applications, yet their evolving structure poses challenges for learning representations that remain consistent and interpret…
cs.LG2024
Model Compression Method for S4 with Diagonal State Space Layers using Balanced Truncation
Haruka Ezoe, Kazuhiro Sato
To implement deep learning models on edge devices, model compression methods have been widely recognized as useful. However, it remains unclear which model compression methods are…