Researchers have developed new analytical and particle-based methods for uncertainty propagation in random neural network models. These methods leverage the piecewise-linear structure of the Leaky ReLU activation function to approximate the neural network's output, enabling the computation of analytical expressions for its probability density and characteristic functions. The framework has been extended to autoregressive models representing dynamical systems, with numerical experiments on the Lorenz-63 system and Kuramoto-Sivashinsky equation demonstrating accurate uncertainty propagation. AI
IMPACT These methods could improve the reliability and interpretability of neural networks in complex dynamical systems.
RANK_REASON The cluster contains a research paper detailing new methods for uncertainty propagation in neural networks.
Read on arXiv cs.NE (Neural & Evolutionary) →
- arXiv
- Kuramoto--Sivashinsky equation
- Leaky_ReLU
- alphaXiv
- CatalyzeX Code Finder for Papers
- Connected Papers
- CORE Recommender
- DagsHub
- Gotit.pub
- Hugging Face
- Litmaps
- ScienceCast
- scite Smart Citations
AI-generated summary · Google Gemini · from 2 sources. How we write summaries →