no code implementations • 10 Jan 2022 • Masahiro Yukawa, Hiroyuki Kaneko, Kyohei Suzuki, Isao Yamada
We present an efficient mathematical framework based on the linearly-involved Moreau-enhanced-over-subspace (LiMES) model.
no code implementations • 17 Sep 2021 • Tatsuya Koyakumaru, Masahiro Yukawa, Eduardo Pavez, Antonio Ortega
This paper presents a convex-analytic framework to learn sparse graphs from data.
no code implementations • SEMEVAL 2020 • Ran Iwamoto, Masahiro Yukawa
This paper describes the model proposed and submitted by our RIJP team to SemEval 2020 Task1: Unsupervised Lexical Semantic Change Detection.
no code implementations • NeurIPS 2018 • Motoya Ohnishi, Masahiro Yukawa, Mikael Johansson, Masashi Sugiyama
Motivated by the success of reinforcement learning (RL) for discrete-time tasks such as AlphaGo and Atari games, there has been a recent surge of interest in using RL for continuous-time control of physical systems (cf.
no code implementations • 1 Nov 2017 • Daniyal Amir Awan, Renato L. G. Cavalcante, Masahiro Yukawa, Slawomir Stanczak
We propose a novel online learning based detection for the NOMA uplink.
no code implementations • 14 Oct 2014 • Masa-aki Takizawa, Masahiro Yukawa, Cedric Richard
This paper presents a stochastic behavior analysis of a kernel-based stochastic restricted-gradient descent method.
no code implementations • 5 Aug 2014 • Masahiro Yukawa
We propose a novel adaptive learning algorithm based on iterative orthogonal projections in the Cartesian product of multiple reproducing kernel Hilbert spaces (RKHSs).
no code implementations • 3 Apr 2014 • Martin Kasparick, Renato L. G. Cavalcante, Stefan Valentin, Slawomir Stanczak, Masahiro Yukawa
In this paper, we address the problem of reconstructing coverage maps from path-loss measurements in cellular networks.