Placeto learns generalizable RL policies for device placement via iterative improvements and graph embeddings, needing up to 6.1x fewer steps than prior methods and applying to unseen graphs without retraining.
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UNVERDICTED 3representative citing papers
Larch uses a GNN-MDP formulation and a selectivity predictor plus dynamic programming to reorder semantic filter evaluation, cutting token usage 3x-19x versus prior systems on real and synthetic workloads.
AutoPilot uses decentralized reinforcement learning to continuously adjust BFT protocol parameters online, achieving 49.8% lower end-to-end latency than static defaults in dynamic environments.
citing papers explorer
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Placeto: Learning Generalizable Device Placement Algorithms for Distributed Machine Learning
Placeto learns generalizable RL policies for device placement via iterative improvements and graph embeddings, needing up to 6.1x fewer steps than prior methods and applying to unseen graphs without retraining.
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Larch: Learned Query Optimization for Semantic Predicates
Larch uses a GNN-MDP formulation and a selectivity predictor plus dynamic programming to reorder semantic filter evaluation, cutting token usage 3x-19x versus prior systems on real and synthetic workloads.
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AutoPilot: Learning to Steer High Speed Robust BFT
AutoPilot uses decentralized reinforcement learning to continuously adjust BFT protocol parameters online, achieving 49.8% lower end-to-end latency than static defaults in dynamic environments.