97 citations
- Denso IT Laboratory Inc. (Japan)JP4 papers
- Denso (Germany)DE3 papers
- Denso (Japan)JP3 papers
- National Institute of Advanced Industrial Science and TechnologyJP3 papers
- Tokyo Institute of TechnologyJP3 papers
- Nitto (Japan)JP2 papers
- Tohoku UniversityJP2 papers
- Harvard University PressUS1 paper
- Institut Denis PoissonFR1 paper
- Institute of Space and Astronautical ScienceJP1 paper
- Japan Advanced Institute of Science and TechnologyJP1 paper
- Laboratoire de Mathématiques et Physique ThéoriqueFR1 paper
20 papers
Spatially inhomogeneous confinement-deconfinement phase transition in rotating QGP
V. V. Braguta, M. N. Chernodub, Ya. A. Gershtein +1
Using first-principles numerical simulations, we find a new spatially inhomogeneous phase in a rotating gluon plasma. This mixed phase simultaneously contains regions of both confi…
Subsampling Factorization Machine Annealing
Yusuke Hama, Tadashi Kadowaki
Quantum computing and machine learning are state-of-the-art technologies that have been investigated intensively in both academia and industry. The hybrid technology of these two i…
The Japanese Vision for the Black Hole Explorer Mission
Kazunori Akiyama, Kotaro Niinuma, Kazuhiro Hada +43
The Black Hole Explorer (BHEX) is a next-generation space very long baseline interferometry (VLBI) mission concept that will extend the ground-based millimeter/submillimeter arrays…
A Federated Learning Benchmark on Tabular Data: Comparing Tree-Based Models and Neural Networks
William Lindskog, Christian Prehofer
Federated Learning (FL) has lately gained traction as it addresses how machine learning models train on distributed datasets. FL was designed for parametric models, namely Deep Neu…
Histogram-Based Federated XGBoost using Minimal Variance Sampling for Federated Tabular Data
William Lindskog, Christian Prehofer, Sarandeep Singh
Federated Learning (FL) has gained considerable traction, yet, for tabular data, FL has received less attention. Most FL research has focused on Neural Networks while Tree-Based Mo…
Federated Learning for Tabular Data using TabNet: A Vehicular Use-Case
William Lindskog, Christian Prehofer
In this paper, we show how Federated Learning (FL) can be applied to vehicular use-cases in which we seek to classify obstacles, irregularities and pavement types on roads. Our pro…