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个人简介
I study the theoretical aspects of deep learning with a particular focus on understanding the Emergence of Structure in Deep Learning. A major aspect of my research focus on the geometric and statistical structure (e.g., clustering, disentanglement, low dimensionality, sparsity) that appear in neural networks in different learning regimes. I investigate how the learning task, the optimization process, and architectural choices promote these structures, as well as their relationship to different types of generalization (e.g., adaptivity, transferability, compositionality, out of distribution, etc'). My work combines rigorous empirical experimentation with mathematical analysis of practical deep learning regimes.
研究兴趣
论文共 42 篇作者统计合作学者相似作者
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arxiv(2023)
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ICML 2023pp.28729-28745, (2023)
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arxiv(2023)
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arXiv (Cornell University) (2023)
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arXiv (Cornell University) (2023)
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D-Core
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