EstGraph benchmark evaluates LLMs on estimating properties of very large graphs from random-walk samples that fit in context limits.
Graph neural networks: A review of methods and applications
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GraphStar is a new GNN that adds star nodes and relay attention to achieve non-local representations for node, graph, and link tasks, claiming 2-5% gains over prior SOTA on benchmarks.
Montparnasse Monte Carlo framework solves all Eterna100 V1 puzzles faster than DesiRNA and finds mRNA sequences with more paired bases than LinearDesign's MFE solution.
Techniques enable training the sparse GNN from Allamanis et al. [2018] on dense TPU hardware in 13 minutes versus a full day originally.
A review summarizing machine learning methods for multi-messenger probes of dark matter and new physics, with a proposed plan for future integrated analyses.
citing papers explorer
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Evaluating LLMs on Large-Scale Graph Property Estimation via Random Walks
EstGraph benchmark evaluates LLMs on estimating properties of very large graphs from random-walk samples that fit in context limits.
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Graph Star Net for Generalized Multi-Task Learning
GraphStar is a new GNN that adds star nodes and relay attention to achieve non-local representations for node, graph, and link tasks, claiming 2-5% gains over prior SOTA on benchmarks.
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The Montparnasse Algorithm for RNA Design
Montparnasse Monte Carlo framework solves all Eterna100 V1 puzzles faster than DesiRNA and finds mRNA sequences with more paired bases than LinearDesign's MFE solution.
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Fast Training of Sparse Graph Neural Networks on Dense Hardware
Techniques enable training the sparse GNN from Allamanis et al. [2018] on dense TPU hardware in 13 minutes versus a full day originally.
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Machine Learning for Multi-messenger Probes of New Physics and Cosmology: A Review and Perspective
A review summarizing machine learning methods for multi-messenger probes of dark matter and new physics, with a proposed plan for future integrated analyses.