3 citations · 7 across the 6 of their papers we have counts for
6 papers
Tensor-Network Population Annealing
Takumi Oshima, Yuma Ichikawa, Koji Hukushima
We propose a hybrid sampling method, tensor-network population annealing (TNPA), which combines tensor-network (TN) initialization with population annealing (PA). We apply this met…
Ratio Divergence Learning Using Target Energy in Restricted Boltzmann Machines: Beyond Kullback--Leibler Divergence Learning
Yuichi Ishida, Yuma Ichikawa, Aki Dote +2
We propose ratio divergence (RD) learning for discrete energy-based models, a method that utilizes both training data and a tractable target energy function. We apply RD learning t…
Statistical Mechanics of Min-Max Problems
Yuma Ichikawa, Koji Hukushima
Min-max optimization problems, also known as saddle point problems, have attracted significant attention due to their applications in various fields, such as fair beamforming, gene…
Adaptive Flip Graph Algorithm for Matrix Multiplication
Yamato Arai, Yuma Ichikawa, Koji Hukushima
This study proposes the "adaptive flip graph algorithm", which combines adaptive searches with the flip graph algorithm for finding fast and efficient methods for matrix multiplica…
Learning Dynamics in Linear VAE: Posterior Collapse Threshold, Superfluous Latent Space Pitfalls, and Speedup with KL Annealing
Yuma Ichikawa, Koji Hukushima
Variational autoencoders (VAEs) face a notorious problem wherein the variational posterior often aligns closely with the prior, a phenomenon known as posterior collapse, which hind…
High-dimensional Asymptotics of VAEs: Threshold of Posterior Collapse and Dataset-Size Dependence of Rate-Distortion Curve
Yuma Ichikawa, Koji Hukushima
In variational autoencoders (VAEs), the variational posterior often collapses to the prior, known as posterior collapse, which leads to poor representation learning quality. An adj…