13 papers
Fast and Accurate Prediction of Lattice Thermal Conductivity via Machine Learning Surrogates
Zeyu Wang, Shuya Yamazaki, Martin Hoffmann Petersen +11
The appearance of generative models has opened vast chemical spaces in the design of functional materials. Although machine learning interatomic potentials (MLIPs) have substantial…
GIANT - Global Path Integration and Attentive Graph Networks for Multi-Agent Trajectory Planning
Jonas le Fevre Sejersen, Toyotaro Suzumura, Erdal Kayacan
This paper presents a novel approach to multi-robot collision avoidance that integrates global path planning with local navigation strategies, utilizing attentive graph neural netw…
Modeling User Preferences as Distributions for Optimal Transport-Based Cross-Domain Recommendation under Non-Overlapping Settings
Ziyin Xiao, Toyotaro Suzumura
Cross-domain recommender (CDR) systems aim to transfer knowledge from data-rich domains to data-sparse ones, alleviating sparsity and cold-start issues present in conventional sing…
Database and deep-learning scalability of anharmonic phonon properties by automated brute-force first-principles calculations
Masato Ohnishi, Tianqi Deng, Pol Torres +16
Understanding the anharmonic phonon properties of crystal compounds -- such as phonon lifetimes and thermal conductivities -- is essential for investigating and optimizing their th…
SymCERE: Symmetric Contrastive Learning for Robust Review-Enhanced Recommendation
Toyotaro Suzumura, Hisashi Ikari, Hiroki Kanezashi +2
Modern recommendation systems fuse user behavior graphs and review texts but often encounter a "Fusion Gap" caused by False Negatives, Popularity Bias, and Signal Ambiguity. We pro…
NewsReX: A More Efficient Approach to News Recommendation with Keras 3 and JAX
Igor L. R. Azevedo, Toyotaro Suzumura, Yuichiro Yasui
Reproducing and comparing results in news recommendation research has become increasingly difficult. This is due to a fragmented ecosystem of diverse codebases, varied configuratio…