Unraveling the Deep ReLU Network Puzzle
Deep feedforward ReLU networks remain a cornerstone of AI, yet their workings are often opaque. Recent insights into path relationships and input space division offer new clarity.
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New preprint:
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Theoretical Analysis Reveals Why Linear RNNs Are More Parallelizable Than Nonlinear RNNs
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