arXiv (All)
open access
SpikeLite: Lightweight Spiking Neural Networks for Time-Series Forecasting
Spiking neural networks (SNNs) offer an energy-efficient paradigm for time-series forecasting through spike-driven computation. However, recent SNN forecasters often pursue higher accuracy through increasingly complex attention mechanisms, or specialized neuronal dynamics, weakening the lightweight
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- SpikF-GO: Spiking Fourier Graph Operators for Multivariate Time Series Forecasting#1012241
- Causal pieces: analysing and improving spiking neural networks piece by piece#1141412
- Large Language Model-Guided Evolutionary Discovery of Native Neural Architectures for Spiking Sequence Modeling#1154519
- On the second-order optimization for spiking neural networks#1077308
- Quantifying How Training Gradient Sparsity Affect Spiking Neural Network Accuracy And Robustness#1128522
Record · ID 1129698
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive