Johns Hopkins UniversityEST. 1876
America’s First Research University
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Title: Mitigating the Curse of Detail: Scaling Arguments for Feature Learning and Sample Complexity Abstract: Two pressing topics in the theory of deep learning are the interpretation of feature learning mechanisms and the determination of the implicit bias of networks in the rich regime. Current theories of rich feature learning effects revolve around networks with […] |
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Title: Beyond Scores: Proximal Diffusion Models Abstract: Diffusion models have quickly become some of the most popular and powerful generative models for high-dimensional data. The key insight that enabled their development was the realization that access to the score— the gradient of the log-density at different noise levels— allows for sampling from data distributions by solving a […] |
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This seminar series probes some of the central questions in the physics of learning: What properties of data make it learnable? How is learned information encoded in neural representations? Do neural networks exhibit universal properties? Can we construct a thermodynamic theory of learning? What determines how performance scales with model size and computational resources? |
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