Learned Wiring architecture boosts sample efficiency in AI prediction tasks

Researchers have introduced Learned Wiring (LeWi), a novel neural network architecture designed to improve sample efficiency in complex prediction tasks. LeWi addresses the curse of dimensionality by learning a low-dimensional embedding for each neuron, restricting connections to nearby neurons, and jointly training embeddings and weights. This approach has demonstrated superior performance on tasks with hierarchical or modular structures, outperforming dense networks and other baselines in sample efficiency and representation alignment. LeWi showed significant gains on 17 world-model prediction tasks, winning most comparisons against dense baselines. AI

IMPACT This architecture could lead to more sample-efficient AI models, reducing the data and computational resources needed for training complex prediction tasks.

RANK_REASON The item is an academic paper detailing a new method for neural network architecture. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• alphaXiv
• CatalyzeX Code Finder for Papers
• CORE Recommender
• CSFRBench
• DagsHub
• Gotit.pub
• Hugging Face
• IArxiv Recommender
• Influence Flower
• Learned Wiring
• Lewis
• ScienceCast

• paper
• model release

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →