3 papers
cs.LG2022
Towards Practical Explainability with Cluster Descriptors
Xiaoyuan Liu, Ilya Tyagin, Hayato Ushijima-Mwesigwa +2
With the rapid development of machine learning, improving its explainability has become a crucial research goal. We study the problem of making the clusters more explainable by inv…
cs.ET2022★ 18 cited
Partitioning Dense Graphs with Hardware Accelerators
Xiaoyuan Liu, Hayato Ushijima-Mwesigwa, Indradeep Ghosh +1
Graph partitioning is a fundamental combinatorial optimization problem that attracts a lot of attention from theoreticians and practitioners due to its broad applications. From mul…
cs.SI2020
Ising-Based Louvain Method: Clustering Large Graphs with Specialized Hardware
Pouya Rezazadeh Kalehbasti, Hayato Ushijima-Mwesigwa, Avradip Mandal +1
Recent advances in specialized hardware for solving optimization problems such quantum computers, quantum annealers, and CMOS annealers give rise to new ways for solving real-word…