arXiv (All)
open access
The Price of Locality: Why Forward-Forward Underperforms Backpropagation?
The Forward-Forward Algorithm (FFA) replaces backpropagation (BP) with layer-wise local contrastive objectives, eliminating the backward pass and the need to retain intermediate activations, yet suffers a persistent performance gap with BP that worsens with depth. This paper diagnoses two structural
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
- The Ups and Downs of Backprop Weights#1037237
- Layers Matter: Why Continual Learning Regularization Should Be Layer-Adaptive#617028
- Layers Matter: Why Continual Learning Regularization Should Be Layer-Adaptive#616528
- Subspace Optimization for Backpropagation-Free Continual Test-Time Adaptation#974369
- Trust the Direction, Search the Step: Zero-and-First-Order Methods for LLM Fine-Tuning#1161018
Record · ID 1117952
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