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Statistical Physics of Machine Learning 2020

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Introductory lectures on first-order convex optimization (Lecture 2) by Praneeth Netrapalli

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Exploring the random landscapes of inference (Lecture 1) by Gérard Ben Arous

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Approximate Message Passing for Statistical Inference and Estimation by Cynthia Rush

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How to Escape Saddle Points Efficiently by Praneeth Netrapalli

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Data assimilation and machine learning by Serge Gratton

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Exploring the random landscapes of inference (Lecture 3) by Gérard Ben Arous

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Analyses of gradient methods for the optimization of wide two layer by Lenaic Chizat

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Optimization with inexact gradient and function by Serge Gratton

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Zeros of polynomials, decay of correlations, and algorithms by Piyush Srivastava

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Exploring the random landscapes of inference (Lecture 2) by Gérard Ben Arous

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Function Entropy in Deep-Learning Networks – Mean Field Behaviour and Large... by David Saad

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Restricted Boltzmann Machines: Stastical Physics and applications... by Simona Cocco

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Random concave functions on an equilateral lattice with periodic hessians by Hariharan Narayanan

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Unraveling the mysteries of stochastic gradient descent for deep...  by Pratik Chaudhari

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Introductory lectures on first-order convex optimization (Lecture 1) by Praneeth Netrapalli

Play