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cs.LG2026
Learning infinite context windows in recurrent architectures via spatial neural computing
Aleix Salvador-Pomarol, Arthur N. Montanari, Earl K. Miller +2
Recurrent neural networks (RNNs) offer linear-time scaling with sequence length while requiring only constant memory, yet they struggle to capture long-range dependencies due to va…
cs.LG2026
Energy-Based Dynamical Models for Neurocomputation, Learning, and Optimization
Arthur N. Montanari, Francesco Bullo, Dmitry Krotov +1
Recent advances at the intersection of control theory, neuroscience, and machine learning have revealed novel mechanisms by which dynamical systems perform computation. These advan…