Research
研究
研究業績
カテゴリー
研究領域
年
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Visual Similarity and Structural Aggregation in Examiner-Citation Networks for GNN Evaluation
Soichi Onozuka, Yohei Kobashi, Takaaki Ohnishi, Yutaka Matsuo
Proceedings of the 25th IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT 2026), Short Paper, December 2026
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Lightweight Ban Prediction in YouTube Live-Stream Chat: Per-Genre Feature Selection and Automatic Genre Assignment
Ryotaro Hamada, Yohei Kobashi, Hiroto Sudo, Yutaka Matsuo
Proceedings of the 25th IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT 2026), Regular Paper, December 2026
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FasTARFlow: Distilling Transformer-based Autoregressive Flows into Fast Inverse Autoregressive Models
Kai Yamashita, Shohei Taniguchi, Masahiro Suzuki, Yutaka Matsuo
Neurocomputing, 2026
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Temporal-Aware Graph Attention for Sequential Knowledge Tracing
Sunil Kumar Maurya, Makoto Kawano, Yusuke Iwasawa, Yutaka Matsuo
Proceedings of the 35th ACM International Conference on Information and Knowledge Management (CIKM 2026), November 2026
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Residual Koopman Spectral Profiling for Predicting and Preventing Transformer Training Instability
Bum Jun Kim, Shohei Taniguchi, Makoto Kawano, Yusuke Iwasawa, Yutaka Matsuo
Proceedings of the 42nd Conference on Uncertainty in Artificial Intelligence (UAI 2026), August 2026
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MMA:Benchmarking Multi-ModalLarge Language Models in Ambiguity Context
Ru Wang*, Selena Song*, Yuquan Wang, Liang Ding, Mingming Gong, Yusuke Iwasawa, Yutaka Matsuo, Jiaxian Guo (*Equal Contribution)
The Third Conference on Parsimony and Learning(CPAL 2026), March 2026
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Beyond In-Distribution Success: Scaling Curves of CoT Granularity for Language Model Generalization
Ru Wang, Wei Huang, Selena Song, Haoyu Zhang, Qian Niu, Yusuke Iwasawa, Yutaka Matsuo, Jiaxian Guo
The Third Conference on Parsimony and Learning(CPAL 2026), March 2026
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Discovery of liquid crystalline polymers with high thermal conductivity using machine learning
Hayato Maeda, Stephen Wu, Rika Marui, Erina Yoshida, Kan Hatakeyama-Sato, Yuta Nabae, Shiori Nakagawa, Meguya Ryu, Ryohei Ishige, Yoh Noguchi
npj Computational Materials, Vol. 11, Issue 1, July 2025
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Portfolio optimization using deep learning with risk aversion utility function
Kenji Kubo, Kei Nakagawa
Finance Research Letters, Vol. 74, 106761, June 2025
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Disappearance of Timestep Embedding: A Case Study on Neural ODE and Diffusion Models
Bum Jun Kim, Yoshinobu Kawahara, Sang Woo Kim
Transactions on Machine Learning Research (TMLR), 2025