Theory Learning weights in networks with delay We start by defining our two differential equations in…
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Bayesian continual learning and forgetting in neural networks
Description of the variational inference framework Exact computation of the truncated posterior (Eq. (4)) becomes intractable…
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Unifying machine learning and interpolation theory via interpolating neural networks
Discretization of the input domain Consider a regression problem that relates I inputs and L outputs.…
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Coarse-graining network flow through statistical physics and machine learning
Macroscopic indicators for information flow Let G be a network of N nodes and ∣E∣ connections,…
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Hybrid neural networks for continual learning inspired by corticohippocampal circuits
Corticohippocampal recurrent loops for episode learning and generalization Recent research increasingly supports the view that the…
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Reinforcement learning for communication load balancing: approaches and challenges
1. Introduction Wireless communication networks have revolutionized the world, providing reliable high-bandwidth low latency communication through…
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Language-like communication improves learning in artificial networks, finds study
Student feedback alters the language embedding according to a utility function. Credit: Nature Communications (2024). DOI:…
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