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- W2617637038 abstract "Motivated by the idea that criticality and universality of phase transitions might play a crucial role in achieving and sustaining learning and intelligent behaviour in biological and artificial networks, we analyse a theoretical and a pragmatic experimental set up for critical phenomena in deep learning. On the theoretical side, we use results from statistical physics to carry out critical point calculations in feed-forward/fully connected networks, while on the experimental side we set out to find traces of criticality in deep neural networks. This is our first step in a series of upcoming investigations to map out the relationship between criticality and learning in deep networks." @default.
- W2617637038 created "2017-06-05" @default.
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- W2617637038 date "2017-02-26" @default.
- W2617637038 modified "2023-10-17" @default.
- W2617637038 title "Criticality & Deep Learning I: Generally Weighted Nets" @default.
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- W2617637038 doi "https://doi.org/10.48550/arxiv.1702.08039" @default.
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