Neural Networks as Stat Mech Systems Based on arXiv:1710.06570 [stat.ML], “A Correspondence Between Random Neural Networks and Statistical Field Theory” Yoni Kahn, KICP/UIUC hep-ai 1/8/19
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sha1_base64="ah+KiWCtDx9PIzoqxt0DK9fgMP0=">AB73icdVDLSsNAFJ3UV62vqks3g0WoUEoSQ1t3RTeupEJf2IYymU7aoZNJnJkIJfQn3LhQxK2/486/cdJWUNEDFw7n3Mu93gRo1KZ5oeRWVldW9/Ibua2tnd29/L7B20ZxgKTFg5ZKLoekoRTlqKka6kSAo8BjpeJPL1O/cEyFpyJtqGhE3QCNOfYqR0lL3tnhdapeap4N8wSyf1yq2U4Fm2TSrlm2lxK46Zw60tJKiAJZoDPLv/WGI4BwhRmSsmeZkXITJBTFjMxy/ViSCOEJGpGephwFRLrJ/N4ZPNHKEPqh0MUVnKvfJxIUSDkNPN0ZIDWv71U/MvrxcqvuQnlUawIx4tFfsygCmH6PBxSQbBiU0QFlTfCvEYCYSVjinQ/j6FP5P2nbZ0vzGKdQvlnFkwRE4BkVgSqogyvQAC2AQMP4Ak8G3fGo/FivC5aM8Zy5hD8gPH2CZrCjwc=</latexit> <latexit sha1_base64="ah+KiWCtDx9PIzoqxt0DK9fgMP0=">AB73icdVDLSsNAFJ3UV62vqks3g0WoUEoSQ1t3RTeupEJf2IYymU7aoZNJnJkIJfQn3LhQxK2/486/cdJWUNEDFw7n3Mu93gRo1KZ5oeRWVldW9/Ibua2tnd29/L7B20ZxgKTFg5ZKLoekoRTlqKka6kSAo8BjpeJPL1O/cEyFpyJtqGhE3QCNOfYqR0lL3tnhdapeap4N8wSyf1yq2U4Fm2TSrlm2lxK46Zw60tJKiAJZoDPLv/WGI4BwhRmSsmeZkXITJBTFjMxy/ViSCOEJGpGephwFRLrJ/N4ZPNHKEPqh0MUVnKvfJxIUSDkNPN0ZIDWv71U/MvrxcqvuQnlUawIx4tFfsygCmH6PBxSQbBiU0QFlTfCvEYCYSVjinQ/j6FP5P2nbZ0vzGKdQvlnFkwRE4BkVgSqogyvQAC2AQMP4Ak8G3fGo/FivC5aM8Zy5hD8gPH2CZrCjwc=</latexit> Motivation Neural networks have extremely large numbers of parameters. On the surface, that makes them hard to understand analytically. But physics provides examples where systems simplify in the limit of large numbers of constituents: thermodynamics/stat mech. N ∼ 10 23 Z ( N, V, T ) Ψ ( x 1 , x 2 , . . . , x N )
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