{"id":710308,"date":"2020-12-05T20:05:39","date_gmt":"2020-12-06T04:05:39","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/?post_type=msr-research-item&p=710308"},"modified":"2022-03-10T10:50:53","modified_gmt":"2022-03-10T18:50:53","slug":"tensor-programs-iii-neural-matrix-laws","status":"publish","type":"msr-research-item","link":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/publication\/tensor-programs-iii-neural-matrix-laws\/","title":{"rendered":"Tensor Programs III: Neural Matrix Laws."},"content":{"rendered":"

In a neural network (NN), weight matrices<\/em> linearly transform inputs into preactivations<\/em> that are then transformed nonlinearly into activations<\/em>. A typical NN interleaves multitudes of such linear and nonlinear transforms to express complex functions. Thus, the (pre-)activations depend on the weights in an intricate manner. We show that, surprisingly, (pre-)activations of a randomly initialized NN become independent<\/em>\u00a0from the weights as the NN’s widths tend to infinity, in the sense of asymptotic freeness in random matrix theory. We call this the Free Independence Principle (FIP), which has these consequences: 1) It rigorously justifies the calculation of asymptotic Jacobian singular value distribution of an NN in Pennington et al. [36,37], essential for training ultra-deep NNs [48]. 2) It gives a new justification of gradient independence assumption used for calculating the Neural Tangent Kernel of a neural network. FIP and these results hold for any neural architecture. We show FIP by proving a Master Theorem for any Tensor Program, as introduced in Yang [50,51], generalizing the Master Theorems proved in those works. As warmup demonstrations of this new Master Theorem, we give new proofs of the semicircle and Marchenko-Pastur laws, which benchmarks our framework against these fundamental mathematical results.<\/p>\n","protected":false},"excerpt":{"rendered":"

In a neural network (NN), weight matrices linearly transform inputs into preactivations that are then transformed nonlinearly into activations. A typical NN interleaves multitudes of such linear and nonlinear transforms to express complex functions. Thus, the (pre-)activations depend on the weights in an intricate manner. We show that, surprisingly, (pre-)activations of a randomly initialized NN […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"user_nicename","value":"Greg 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