基本信息
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职业迁徙
个人简介
My research interest is to develop probabilistic modeling approaches and scalable and efficient inference algorithms, with applications to neural and behavior analyses, as well as many real-world problems, e.g. time series, geospatial data, speech data.
More specifically, the modeling topics involve: deep generative models, variational autoencoder, (deep) Gaussian process, Bayesian (convolutional) neural net, Bayesian nonparametric, Bayesian optimization and active learning, computer vision, hierarchical spatial and temporal models, latent dynamic models, (inverse) reinforcement learning, etc.
The applications cover but are not limited to: neural latent discovery, 3D full-body kinematic model estimation, identifying behavior syllables, studying intrinsic motives and reward representations of animal and human behaviors, fMRI decoding, neural sensory encoding, optimal experimental design, etc.
研究兴趣
论文共 23 篇作者统计合作学者相似作者
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CoRR (2024)
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CoRR (2024)
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ICLR 2024 (2023)
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arXiv (Cornell University) (2023)
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arXiv (Cornell University) (2023)
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CoRR (2023)
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CoRR (2023)
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arxiv(2022)
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D-Core
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