1 citations · 1 across the 4 of their papers we have counts for
6 papers
Improving Generalization by Permutation Routing Across Model Copies
Shuhei Kashiwamura, Timothee Leleu
We introduce a use of the \(M\)-cover (or \(M\)-layer) transform for machine learning. The method replicates a model \(M\) times, but instead of coupling the copies through paramet…
Uncertainty-Aware Sparse Identification of Dynamical Systems via Bayesian Model Averaging
Shuhei Kashiwamura, Yusuke Kato, Hiroshi Kori +1
In many problems of data-driven modeling for dynamical systems, the governing equations are not known a priori and must be selected phenomenologically from a large set of candidate…
High-Dimensional Learning Dynamics of Quantized Models with Straight-Through Estimator
Yuma Ichikawa, Shuhei Kashiwamura, Ayaka Sakata
Quantized neural network training optimizes a discrete, non-differentiable objective. The straight-through estimator (STE) enables backpropagation through surrogate gradients and i…
Bayesian estimation of coupling strength and heterogeneity in a coupled oscillator model from macroscopic quantities
Yusuke Kato, Shuhei Kashiwamura, Emiri Watanabe +2
Various macroscopic oscillations, such as the heartbeat and the flashing of fireflies, are created by synchronizing oscillatory units (oscillators). To elucidate the mechanism of s…
Mesoscopic Bayesian Inference by Solvable Models
Shun Katakami, Shuhei Kashiwamura, Kenji Nagata +2
The rapid advancement of data science and artificial intelligence has affected physics in numerous ways, including the application of Bayesian inference, setting the stage for a re…
Effect of Weight Quantization on Learning Models by Typical Case Analysis
Shuhei Kashiwamura, Ayaka Sakata, Masaaki Imaizumi
This paper examines the quantization methods used in large-scale data analysis models and their hyperparameter choices. The recent surge in data analysis scale has significantly in…