W. Bialek - Towards a renormalization group for networks of neurons Published -- Download video MP4 360p Recommendations 59:19 A. Radenovic - Nanopores in 2D Materials 25:28 Dendrites: Why Biological Neurons Are Deep Neural Networks 1:51:06 Physics of Life: A survey from the US National Academy of Sciences by William Bialek 41:59 Renormalization Group Theory and Machine Learning | AI & Physics | Giulio Biroli 16:37 [Old] Renormalization: Why Bigger is Simpler 12:37 Chaos Theory: the language of (in)stability 38:27 ICLR 2021 Keynote - "Geometric Deep Learning: The Erlangen Programme of ML" - M Bronstein 1:18:03 Recent progress in statistical mechanics for networks of real neurons - William Bialek 20:55 Renormalization: The Art of Erasing Infinity 37:05 Brain Criticality - Optimizing Neural Computations 1:24:44 Stanford Seminar - Information Theory of Deep Learning, Naftali Tishby 48:38 Florian Marquardt - How a physical system can be turned into a self-learning machine 1:29:28 Lecture 1: Coarse-Graining, Renormalization & Universality 10:31 Prof. William Bialek on Future Challenges in Biophysics 50:20 Princeton's William Happer rebuts myth of carbon pollution 1:41:50 Statistical physics of biological systems: From molecules to minds - 1 of 4 17:02 William Bialek (Princeton): Developing Unifying Theories for Biology Similar videos 1:11:42 Statistical Mechanics for Networks of Real Neurons - Dr.Bialek 01:01 The Renormalisation Group 1:27:22 William Bialek, RG-inspired approaches to the analysis of real neural networks, July 19th 1:12:46 Bill Bialek (Princeton University) 16:37 Renormalization: Why Bigger is Simpler 01:53 Observing multiple neurons simultaneously 1:30:27 William Bialek: Networks, Codes & Information Flow 03:02 2017 李水清物理講座 (William Bialek) 58:25 CNS*2021 Keynote, William Bialek (K2) 1:04:42 Lecture 27: Renormalization and envelopes 24:45 Why Deep Learning Works: Perspectives from Theoretical Chemistry, Charles Martin 24:19 Local and functional renormalization group More results