76 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- The University of TokyoJP8 papers
- National Institute of Advanced Industrial Science and TechnologyJP6 papers
- Tokyo Institute of TechnologyJP4 papers
- Chiba Institute of TechnologyJP2 papers
- Institute of Space and Astronautical ScienceJP2 papers
- Planetary Science InstituteUS2 papers
- Aichi Toho UniversityJP1 paper
- Artificial Intelligence Research CenterJP1 paper
- Astronomical Society of AustraliaAU1 paper
- Brown UniversityUS1 paper
- California University of PennsylvaniaUS1 paper
- Centre National de la Recherche ScientifiqueFR1 paper
5 papers · 1 filter
Quasi-ballistic heat conduction due to Lévy phonon flights in silicon nanowires
Roman Anufriev, Sergei Gluchko, Sebastian Volz +1
Future of silicon-based microelectronics relies on solving the heat dissipation problem. A solution may lie in a nanoscale phenomenon known as ballistic heat conduction, which impl…
On -hyperparameter Learning via Bilevel Nonsmooth Optimization
Takayuki Okuno, Akiko Takeda, Akihiro Kawana +1
We propose a bilevel optimization strategy for selecting the best hyperparameter value for the nonsmooth regularizer with . The concerned bilevel optimization pr…
An Analytic Solution to the Inverse Ising Problem in the Tree-reweighted Approximation
Takashi Sano
Many iterative and non-iterative methods have been developed for inverse problems associated with Ising models. Aiming to derive an accurate non-iterative method for the inverse pr…
A constant-ratio approximation algorithm for a class of hub-and-spoke network design problems and metric labeling problems: star metric case
Yuko Kuroki, Tomomi Matsui
Transportation networks frequently employ hub-and-spoke network architectures to route flows between many origin and destination pairs. Hub facilities work as switching points for…
Robust and sparse Gaussian graphical modeling under cell-wise contamination
Shota Katayama, Hironori Fujisawa, Mathias Drton
Graphical modeling explores dependences among a collection of variables by inferring a graph that encodes pairwise conditional independences. For jointly Gaussian variables, this t…