2 citations · 2 across the 4 of their papers we have counts for
6 papers
Communication Compression for Decentralized Learning with Operator Splitting Methods
Yuki Takezawa, Kenta Niwa, Makoto Yamada
In decentralized learning, operator splitting methods using a primal-dual formulation (e.g., the Edge-Consensus Learning (ECL)) has been shown to be robust to heterogeneous data an…
Revisiting the Primal-Dual Method of Multipliers for Optimisation over Centralised Networks
Guoqiang Zhang, Kenta Niwa, W. Bastiaan Kleijn
The primal-dual method of multipliers (PDMM) was originally designed for solving a decomposable optimisation problem over a general network. In this paper, we revisit PDMM for opti…
Approximated Orthonormal Normalisation in Training Neural Networks
Guoqiang Zhang, Kenta Niwa, W. B. Kleijn
Generalisation of a deep neural network (DNN) is one major concern when employing the deep learning approach for solving practical problems. In this paper we propose a new techniqu…
Rapidly Adapting Moment Estimation
Guoqiang Zhang, Kenta Niwa, W. Bastiaan Kleijn
Adaptive gradient methods such as Adam have been shown to be very effective for training deep neural networks (DNNs) by tracking the second moment of gradients to compute the indiv…
DNN-based Source Enhancement to Increase Objective Sound Quality Assessment Score
Yuma Koizumi, Kenta Niwa, Yusuke Hioka +2
We propose a training method for deep neural network (DNN)-based source enhancement to increase objective sound quality assessment (OSQA) scores such as the perceptual evaluation o…
Bregman Monotone Operator Splitting
Kenta Niwa, W. Bastiaan Kleijn
Monotone operator splitting is a powerful paradigm that facilitates parallel processing for optimization problems where the cost function can be split into two convex functions. We…