An MPNN-PPO agent with max aggregation and hub-exclusion curriculum outperforms betweenness-centrality heuristics on budgeted max-flow channel placement across real Lightning Network snapshots, and has been deployed in production.
In: International Conference on Learning Representations (ICLR) (2019)
4 Pith papers cite this work. Polarity classification is still indexing.
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2026 4representative citing papers
Separating acoustic and expectation ANN representations as teacher targets improves EEG music identification beyond baselines and seed ensembles.
RAG-DIVE uses an LLM to dynamically generate, validate, and evaluate multi-turn dialogues for assessing RAG system performance in interactive settings.
Proposes SKA observations of El Gordo to study high-redshift ICM magnetic fields via continuum and polarization measurements and test amplification models.
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MPFlow: Learning Budgeted Max-Flow Optimization on the Lightning Network with Deep Graph Reinforcement Learning
An MPNN-PPO agent with max aggregation and hub-exclusion curriculum outperforms betweenness-centrality heuristics on budgeted max-flow channel placement across real Lightning Network snapshots, and has been deployed in production.
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Expectation and Acoustic Neural Network Representations Enhance Music Identification from Brain Activity
Separating acoustic and expectation ANN representations as teacher targets improves EEG music identification beyond baselines and seed ensembles.
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RAG-DIVE: A Dynamic Approach for Multi-Turn Dialogue Evaluation in Retrieval-Augmented Generation
RAG-DIVE uses an LLM to dynamically generate, validate, and evaluate multi-turn dialogues for assessing RAG system performance in interactive settings.
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Probing High-redshift Intracluster Medium Using SKA
Proposes SKA observations of El Gordo to study high-redshift ICM magnetic fields via continuum and polarization measurements and test amplification models.