1 citations · 1 across the 7 of their papers we have counts for
9 papers
Optimizing EEG Graph Structure for Seizure Detection: An Information Bottleneck and Self-Supervised Learning Approach
Lincan Li, Rikuto Kotoge, Xihao Piao +2
Seizure detection from EEG signals is highly challenging due to complex spatiotemporal dynamics and extreme inter-patient variability. To model them, recent methods construct dynam…
ODEBrain: Continuous-Time EEG Graph for Modeling Dynamic Brain Networks
Haohui Jia, Zheng Chen, Lingwei Zhu +6
Modeling neural population dynamics is crucial for foundational neuroscientific research and various clinical applications. Conventional latent variable methods typically model con…
EvoBrain: Dynamic Multi-Channel EEG Graph Modeling for Time-Evolving Brain Networks
Rikuto Kotoge, Zheng Chen, Tasuku Kimura +4
Dynamic GNNs, which integrate temporal and spatial features in Electroencephalography (EEG) data, have shown great potential in automating seizure detection. However, fully capturi…
Data-efficient Targeted Token-level Preference Optimization for LLM-based Text-to-Speech
Rikuto Kotoge, Yuichi Sasaki
Aligning text-to-speech (TTS) system outputs with human feedback through preference optimization has been shown to effectively improve the robustness and naturalness of language mo…
Can Compact Language Models Search Like Agents? Distillation-Guided Policy Optimization for Preserving Agentic RAG Capabilities
Rikuto Kotoge, Mai Nishimura, Jiaxin Ma
Reinforcement Learning has emerged as a dominant post-training approach to elicit agentic RAG behaviors such as search and planning from language models. Despite its success with l…
ExPath: Targeted Pathway Inference for Biological Knowledge Bases via Graph Learning and Explanation
Rikuto Kotoge, Ziwei Yang, Zheng Chen +4
Retrieving targeted pathways in biological knowledge bases, particularly when incorporating wet-lab experimental data, remains a challenging task and often requires downstream anal…
SplitSEE: A Splittable Self-supervised Framework for Single-Channel EEG Representation Learning
Rikuto Kotoge, Zheng Chen, Tasuku Kimura +4
While end-to-end multi-channel electroencephalography (EEG) learning approaches have shown significant promise, their applicability is often constrained in neurological diagnostics…
GeSubNet: Gene Interaction Inference for Disease Subtype Network Generation
Ziwei Yang, Zheng Chen, Xin Liu +5
Retrieving gene functional networks from knowledge databases presents a challenge due to the mismatch between disease networks and subtype-specific variations. Current solutions, i…
MLOmics: Cancer Multi-Omics Database for Machine Learning
Ziwei Yang, Rikuto Kotoge, Xihao Piao +6
Framing the investigation of diverse cancers as a machine learning problem has recently shown significant potential in multi-omics analysis and cancer research. Empowering these su…