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 2851–2860 of 4891 papers

TitleStatusHype
Stabilizing Temporal Difference Learning via Implicit Stochastic Recursion—0
Stacked Generative Machine Learning Models for Fast Approximations of Steady-State Navier-Stokes Equations—0
STARS: Sparse Learning Correlation Filter with Spatio-temporal Regularization and Super-resolution Reconstruction for Thermal Infrared Target Tracking—0
Star with Bilinear Mapping—0
State estimation of a carbon capture process through POD model reduction and neural network approximation—0
Stationary and Sparse Denoising Approach for Corticomuscular Causality Estimation—0
Statistical and computational trade-offs in estimation of sparse principal components—0
Statistical and Computational Trade-offs in Variational Inference: A Case Study in Inferential Model Selection—0
Statistically Consistent Saliency Estimation—0
Statistically Guided Divide-and-Conquer for Sparse Factorization of Large Matrix—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified