Deep Linear Networks
PulseAugur coverage of Deep Linear Networks — every cluster mentioning Deep Linear Networks across labs, papers, and developer communities, ranked by signal.
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Tree Tensor Networks Reveal Benign Loss Landscapes Despite Hard Targets
Researchers have explored the theoretical underpinnings of why deep neural networks, despite their complexity, often learn effectively in practice. A new study using Tree Tensor Networks (TTNs) demonstrates that even mo…
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Grokking explained by two training clocks theory
Researchers have developed a theoretical framework to explain the phenomenon of "grokking" in machine learning, where a model fits training data and learns a generalizable rule at different rates. They propose the conce…
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Gradient Descent with Large Step Size Redistributes Signals in Deep Networks
Researchers have demonstrated that discrete Gradient Descent with a large step size leads to a different outcome than Gradient Flow in deep linear networks with multiple pathways. While Gradient Flow predicts a "winner-…