6 papers
Selective Denoising Diffusion Model for Time Series Anomaly Detection
Kohei Obata, Zheng Chen, Yasuko Matsubara +2
Time series anomaly detection (TSAD) has been an important area of research for decades, with reconstruction-based methods, mostly based on generative models, gaining popularity an…
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…
RepSPD: Enhancing SPD Manifold Representation in EEGs via Dynamic Graphs
Haohui Jia, Zheng Chen, Lingwei Zhu +4
Decoding brain activity from electroencephalography (EEG) is crucial for neuroscience and clinical applications. Among recent advances in deep learning for EEG, geometric learning…
A Survey of Reinforcement Learning for Software Engineering
Dong Wang, Hanmo You, Lingwei Zhu +6
Reinforcement Learning (RL) has emerged as a powerful paradigm for sequential decision-making and has attracted growing interest across various domains, particularly following the…
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…
Towards Physiologically Sensible Predictions via the Rule-based Reinforcement Learning Layer
Lingwei Zhu, Zheng Chen, Yukie Nagai +1
This paper adds to the growing literature of reinforcement learning (RL) for healthcare by proposing a novel paradigm: augmenting any predictor with Rule-based RL Layer (RRLL) that…