Search Results for author: Akinori Tanaka

Found 7 papers, 1 papers with code

Towards reduction of autocorrelation in HMC by machine learning

no code implementations11 Dec 2017 Akinori Tanaka, Akio Tomiya

Our proposing algorithm provides consistent central values of expectation values of the action density and one-point Green's function with ones from the original HMC in both the symmetric phase and broken phase within the statistical error.

BIG-bench Machine Learning

Asymptotic Risk of Bezier Simplex Fitting

no code implementations17 Jun 2019 Akinori Tanaka, Akiyoshi Sannai, Ken Kobayashi, Naoki Hamada

In this paper, we analyze the asymptotic risks of those B\'ezier simplex fitting methods and derive the optimal subsample ratio for the inductive skeleton fitting.

Metric on random dynamical systems with vector-valued reproducing kernel Hilbert spaces

no code implementations17 Jun 2019 Isao Ishikawa, Akinori Tanaka, Masahiro Ikeda, Yoshinobu Kawahara

We empirically illustrate our metric with synthetic data, and evaluate it in the context of the independence test for random processes.

Discriminator optimal transport

1 code implementation NeurIPS 2019 Akinori Tanaka

Within a broad class of generative adversarial networks, we show that discriminator optimization process increases a lower bound of the dual cost function for the Wasserstein distance between the target distribution $p$ and the generator distribution $p_G$.

Approximate Bayesian Computation of Bézier Simplices

no code implementations10 Apr 2021 Akinori Tanaka, Akiyoshi Sannai, Ken Kobayashi, Naoki Hamada

B\'ezier simplex fitting algorithms have been recently proposed to approximate the Pareto set/front of multi-objective continuous optimization problems.

Bézier Flow: a Surface-wise Gradient Descent Method for Multi-objective Optimization

no code implementations23 May 2022 Akiyoshi Sannai, Yasunari Hikima, Ken Kobayashi, Akinori Tanaka, Naoki Hamada

In this paper, we propose a strategy to construct a multi-objective optimization algorithm from a single-objective optimization algorithm by using the B\'ezier simplex model.

PAC learning

Understanding Diffusion Models by Feynman's Path Integral

no code implementations17 Mar 2024 Yuji Hirono, Akinori Tanaka, Kenji Fukushima

Score-based diffusion models have proven effective in image generation and have gained widespread usage; however, the underlying factors contributing to the performance disparity between stochastic and deterministic (i. e., the probability flow ODEs) sampling schemes remain unclear.

Image Generation

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