Learning infinite context windows in recurrent architectures via spatial neural computing
arXiv:2610.10690v1 Announce Type: new Abstract: 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 limita…
Read the full story at arXiv cs.LG ↗
Timeline · 1 report
- 2026-10-09 04:00 · arXiv cs.LG
Learning infinite context windows in recurrent architectures via spatial neural computing