Learning infinite context windows in recurrent architectures via spatial neural computing
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 vanishing gradients and limited receptive fields. To address these limitations, we introduce a second-order recurrent model in which the stand...
Aleix Salvador-Pomarol, A. Montanari, E. K. Miller et al.
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