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Statistical Physics and Inference

  • FDS Colloquium: Jinchao Xu (Kaust), “Finite Element versus Finite Neuron Methods”

    Talk summary: This talk presents a unified framework connecting Barron and Sobolev spaces to analyze the approximation properties of ReLU$^k$ neural networks. It establishes both classical and new sharp approximation rates, showing that for functions in the relevant Barron space, ReLU$^k$ networks can achieve high accuracy without the curse of dimensionality. The same convergence rate…

  • Cancelled: FDS Colloquium: J. Nathan Kutz (UW), “Learning and Data Assimilation of Physics from Videos”

    This event has been cancelled. Talk summary: Sensing is a universal task in science and engineering. Downstream tasks from sensing include learning dynamical models, inferring full state estimates of a system (system identification), control decisions, and forecasting. These tasks are exceptionally challenging to achieve with limited sensors, noisy measurements, and corrupt or missing data. Existing…

  • FDS Colloquium: Mike Winer (IAS), “Heuristic Estimation of Neural Network Outputs”

    Talk summary: Given a neural network and a description of its input distribution, what can we say about the outputs? In some sense we have all the information, but even estimating something like the frequency of a given rare token might require many forward passes. In this talk I discuss approximate techniques for answering these…