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个人简介
My research interests broadly lie in simplifying deep learning. More specifically, I’m interested in developing theory to understand, improve, and simplify empirical deep learning methodology. I work on this problem through the following research threads:
understanding the latent representations learned by modern deep neural networks;
connecting modern deep learning practice to classical signal processing and statistics;
and leveraging such-obtained conceptual insights to design interpretable, efficient, and principled learning algorithms.
I’m particularly interested in problem instances where data is high-dimensional yet has rich structure, such as computer vision, natural language processing, and multi-modal contexts.
understanding the latent representations learned by modern deep neural networks;
connecting modern deep learning practice to classical signal processing and statistics;
and leveraging such-obtained conceptual insights to design interpretable, efficient, and principled learning algorithms.
I’m particularly interested in problem instances where data is high-dimensional yet has rich structure, such as computer vision, natural language processing, and multi-modal contexts.
研究兴趣
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CoRR (2023)
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D-Core
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