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arXiv Machine Learning By Aleix Salvador-Pomarol, Arthur N. Montanari, Earl K. Miller, Adilson E. Motter, Jorge Cort\'es

Learning infinite context windows in recurrent architectures via spatial neural computing

Read the original on arXiv Machine Learning →

The paper proposes a second‑order recurrent neural network that replaces traditional neuron‑to‑neuron communication with a spatially evolving field governed by discretized partial differential equations. This design creates a structured infinite‑order RNN where each state depends on its entire history, providing an unbounded receptive field while keeping the number of parameters fixed. The authors derive conditions for marginal stability that prevent vanishing or exploding gradients, and demonstrate that the architecture outperforms other recurrent models on long‑horizon benchmarks with fewer parameters.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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