arXiv (All)
open access
Wave Function Backpropagation with Explicit Temporal-Interval Dynamics
Conventional neural networks learn predominantly through affine transformations followed by nonlinear activations, while elapsed time is often treated as an auxiliary feature or assumed to be uniformly sampled. This paper introduces Wave Function Backpropagation (WFB), a wave-parameterized learning
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
- Coarse-to-fine multi-resolution hash encoding for implicit full waveform inversion#1156016
- Using Deep Learning to Model Lazy Dynamics for Fast Dynamical Systems#307664
- phys-MCP: A Control Plane for Heterogeneous Physical Neural Networks#168293
- Neural Network Backflow with Low-Rank Multi-Determinant Updates#1062433
- A Cascaded Quantized Spiking Neural Network for Real-Time ECG Arrhythmia Detection on Edge Hardware.#317568
Record · ID 819947
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