A real Schur decomposition projection maps the state matrix of discrete-time state-space layers onto its nearest stable counterpart, delivering accuracy comparable to prior stable identification methods with fewer weights.
Hippo: Recurrent memory with optimal polynomial projections.Advances in neural information processing systems, 33:1474–1487, 2020
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
representative citing papers
In linear recurrent models, infinite-width signal propagation remains accurate only for depths t much smaller than sqrt(width n), with a critical regime at t ~ c sqrt(n) where finite-width effects emerge and dominate for larger t.
Federated learning framework for SNNs that adapts to heterogeneous temporal resolutions via neuron parameter integration, recovering accuracy on SHD and DVS-Gesture under varied mismatch scenarios.
citing papers explorer
-
A Novel Schur-Decomposition-Based Weight Projection Method for Stable State-Space Neural-Network Architectures
A real Schur decomposition projection maps the state matrix of discrete-time state-space layers onto its nearest stable counterpart, delivering accuracy comparable to prior stable identification methods with fewer weights.
-
How Long Does Infinite Width Last? Signal Propagation in Long-Range Linear Recurrences
In linear recurrent models, infinite-width signal propagation remains accurate only for depths t much smaller than sqrt(width n), with a critical regime at t ~ c sqrt(n) where finite-width effects emerge and dominate for larger t.
-
Federated Learning of Spiking Neural Networks under Heterogeneous Temporal Resolutions
Federated learning framework for SNNs that adapts to heterogeneous temporal resolutions via neuron parameter integration, recovering accuracy on SHD and DVS-Gesture under varied mismatch scenarios.
- Continuum: Efficient and Robust Multi-Turn LLM Agent Scheduling with KV Cache Time-to-Live