基本信息
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职业迁徙
个人简介
My research interests lie at the intersection of Machine (Deep) Learning, Computational Neuroscience, and Computer Vision. I am curious about how deep learning could be used to accelerate scientific discovery, and thus how to design better deep learning models for science. The research projects that I worked on, or am actively working on include:
Representation learning: Developing self-supervised learning and generative learning methods to obtain robust representation of neural data without relying on explicit human annotation.
Interpretable deep learning methods: Building deep learning architectures that are interpretable and identifiable under certain pre-defined conditions or assumptions.
Image segmentation: Efficient methods for image segmentation with limited or noisy annotations, explicit constraints, or topological/geometrical priors.
Representation learning: Developing self-supervised learning and generative learning methods to obtain robust representation of neural data without relying on explicit human annotation.
Interpretable deep learning methods: Building deep learning architectures that are interpretable and identifiable under certain pre-defined conditions or assumptions.
Image segmentation: Efficient methods for image segmentation with limited or noisy annotations, explicit constraints, or topological/geometrical priors.
研究兴趣
论文共 16 篇作者统计合作学者相似作者
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ICLR 2024 (2024)
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Chiraag Kaushik,Ran Liu,Chi-Heng Lin, Amrit Khera, Matthew Y Jin, Wenrui Ma,Vidya Muthukumar,Eva L Dyer
CoRR (2024)
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ICML 2023 (2023): 1341-1360
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Jorge Quesada,Lakshmi Sathidevi,Ran Liu,Nauman Ahad, Joy M Jackson,Mehdi Azabou,Jingyun Xiao, Christopher Liding, Matthew Jin,Carolina Urzay,William Gray-Roncal,Erik C Johnson,
arxiv(2022)
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