Scientist, Allen Institute
1 paper at NeurIPS 2025
We analytically predict the exact critical transition to chaos in finite-size heavy-tailed RNNs and reveal a tradeoff: their broader edge-of-chaos regime comes at the cost of lower attractor dimensionality compared to Gaussian networks.