Get Started
Research questionHow can deep continuous-time recurrent networks avoid depth-induced gradient attenuation under truncated temporal backpropagation?In deep continuous-time recurrent stacks, temporal integration can delay lower-layer signals while weakening top-down learning signals across depth. Truncating temporal backpropagation can make this optimization problem more pronounced.
AI
Machine Learning
Neural and Evolutionary Computing
Research Paper
Latest papersRecent research connected to this question, newest first.Prospective Coding Improves Learning in Deep Continuous-Time Recurrent NetworksThe evidence covers Recursive Quadrature Filters, diagonal state-space models, S5, and ORGaNICs under full BPTT and spatial-only backpropagation, with results on raw-audio Speech Commands and Path-X.research paper · Sep 3, 2026
Related questions
How can multilayer RNNs learn nonlinear quadrotor dynamics without vanishing or exploding gradients?How can we simulate and backpropagate through long coupled dynamical systems without stepwise time recursion?How can residual networks approximate high-dimensional semilinear heat-equation solutions without exponential parameter growth?How can low-precision recurrent-state storage preserve small updates during temporal inference?
Home
Topics
Search
Library