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 3981–3990 of 4891 papers

TitleStatusHype
Gaze-based dual resolution deep imitation learning for high-precision dexterous robot manipulation—0
Comparing hundreds of machine learning classifiers and discrete choice models in predicting travel behavior: an empirical benchmark—0
A Multiscale Environment for Learning by DiffusionCode0
Improving Human Decision-Making by Discovering Efficient Strategies for Hierarchical Planning—0
Recurrent Localization Networks applied to the Lippmann-Schwinger EquationCode0
Decision Machines: Congruent Decision Trees—0
Prediction of 3D Cardiovascular hemodynamics before and after coronary artery bypass surgery via deep learning—0
Data-driven sparse polynomial chaos expansion for models with dependent inputs—0
ZeRO-Offload: Democratizing Billion-Scale Model TrainingCode0
Frequency-weighted H2-optimal model order reduction via oblique projection—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified