#sparse-networks News & Analysis

3 articles tagged with #sparse-networks. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

3 articles

AINeutralarXiv – CS AI · 4d ago7/10

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Hierarchical Sparse Circuit Extraction from Billion-Parameter Language Models through Scalable Attribution Graph Decomposition

Researchers introduce Hierarchical Attribution Graph Decomposition (HAGD), a novel method for extracting sparse circuits from billion-parameter language models that reduces computational complexity from exponential to polynomial time. The approach successfully identifies interpretable pathways in models ranging from GPT-2 to Llama-70B, achieving 91% behavioral preservation on modular arithmetic tasks while existing methods like ACDC become memory-prohibitive at 1.4B parameters.

🧠 Llama

AINeutralarXiv – CS AI · 4d ago6/10

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A Hybrid TGN-SEAL Model for Dynamic Graph Link Prediction

Researchers present a hybrid TGN-SEAL model that improves link prediction in dynamic, sparse networks by combining Temporal Graph Networks with enclosing subgraph extraction. The approach achieves at least 2% average precision improvement over standard TGNs on sparse datasets like CDRs and email networks, addressing a key limitation in temporal graph analysis.

AINeutralarXiv – CS AI · May 126/10

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What If We Let Forecasting Forget? A Sparse Bottleneck for Cross-Variable Dependencies

Researchers introduce MS-FLOW, a machine learning framework that improves multivariate time series forecasting by using sparse, selective connections between variables rather than dense interactions. The approach addresses the problem of spurious correlations that plague existing methods, achieving state-of-the-art accuracy on 12 benchmarks while identifying fewer but more reliable dependencies.