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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 4011–4020 of 4891 papers

TitleStatusHype
Data-driven operator learning for energy-efficient building control—0
Data-Driven Outage Restoration Time Prediction via Transfer Learning with Cluster Ensembles—0
Data-driven Power Flow Linearization: Simulation—0
Data-driven Power Flow Linearization: Theory—0
Data-driven Power Loss Identification through Physics-Based Thermal Model Backpropagation—0
Data-driven sparse polynomial chaos expansion for models with dependent inputs—0
Data-Driven Surrogate Modeling Techniques to Predict the Effective Contact Area of Rough Surface Contact Problems—0
Data-Driven Threshold Machine: Scan Statistics, Change-Point Detection, and Extreme Bandits—0
Data-Efficient Interactive Multi-Objective Optimization Using ParEGO—0
Data-Enabled Predictive Control for Flexible Spacecraft—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified