SOTAVerified

Computational Efficiency

Methods and optimizations to reduce the computational resources (e.g., time, memory, or power) needed for training and inference in models. This involves techniques that streamline processing, optimize algorithms, or leverage hardware to enhance performance without compromising accuracy.

Papers

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Showing 521–530 of 4891 papers

TitleStatusHype
Robo-taxi Fleet Coordination at Scale via Reinforcement LearningCode1
A Control-Oriented Simplified Single Particle Model with Grouped Parameter and Sensitivity Analysis for Lithium-Ion BatteriesCode1
Hybrid Temporal Differential Consistency Autoencoder for Efficient and Sustainable Anomaly Detection in Cyber-Physical Systems—0
Sparse Optimization for Transfer Learning: A L0-Regularized Framework for Multi-Source Domain Adaptation—0
Embedded Federated Feature Selection with Dynamic Sparse Training: Balancing Accuracy-Cost Tradeoffs—0
Neural network-enhanced integrators for simulating ordinary differential equations—0
Constrained Gaussian Process Motion Planning via Stein Variational Newton Inference—0
Federated Learning for Medical Image Classification: A Comprehensive Benchmark—0
AI2STOW: End-to-End Deep Reinforcement Learning to Construct Master Stowage Plans under Demand UncertaintyCode0
Planning Safety Trajectories with Dual-Phase, Physics-Informed, and Transportation Knowledge-Driven Large Language ModelsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified