RLGT is a modular reinforcement learning framework for extremal graph theory that handles undirected, directed, looped, and multi-colored graphs to facilitate future research.
International Journal of Computer Vision , year =
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
A hybrid genetic algorithm with model transformations generates families of RL training environments, demonstrated for wildfire mitigation and curriculum learning.
SGER applies a two-phase curriculum to fine-tune LLMs for name matching, reporting 99.02% accuracy and 0.994 F1 on 50,000 real-world Indian name pairs while outperforming baselines and deploying in production.
Neural network learning opacity stems from three dynamical complexity properties in training, rendering some sources of opacity irreducible.
citing papers explorer
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RLGT: A reinforcement learning framework for extremal graph theory
RLGT is a modular reinforcement learning framework for extremal graph theory that handles undirected, directed, looped, and multi-colored graphs to facilitate future research.
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A Model-Driven Approach for Developing Families of Reinforcement Learning Environments
A hybrid genetic algorithm with model transformations generates families of RL training environments, demonstrated for wildfire mitigation and curriculum learning.
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Structure-Guided Entity Resolution: Fine-Tuning LLMs for Robust Name Matching in Complex Linguistic Contexts
SGER applies a two-phase curriculum to fine-tune LLMs for name matching, reporting 99.02% accuracy and 0.994 F1 on 50,000 real-world Indian name pairs while outperforming baselines and deploying in production.
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How Complexity Contributes to Learning Opacity in Machine Learning
Neural network learning opacity stems from three dynamical complexity properties in training, rendering some sources of opacity irreducible.