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Applying statistical learning theory to deep learning
Although statistical learning theory provides a robust framework to understand supervised learning, many theoretical aspects of deep learning remain unclear, in particular how different architectures may lead to inductive bias when trained using gradient based methods.
Machine Learning Without a Processor: Emergent Learning in a Nonlinear Electronic Metamaterial
Standard deep learning algorithms require differentiating large nonlinear networks, a process that is slow and power-hungry.
Desynchronous Learning in a Physics-Driven Learning Network
In a neuron network, synapses update individually using local information, allowing for entirely decentralized learning.
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