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 2861–2870 of 4891 papers

TitleStatusHype
Statistical Optimality and Computational Efficiency of Nyström Kernel PCA—0
Steerable Pyramid Weighted Loss: Multi-Scale Adaptive Weighting for Semantic Segmentation—0
STEI-PCN: an efficient pure convolutional network for traffic prediction via spatial-temporal encoding and inferring—0
Stellar parameter prediction and spectral simulation using machine learning—0
STEPS: Sequential Probability Tensor Estimation for Text-to-Image Hard Prompt Search—0
Stiff Transfer Learning for Physics-Informed Neural Networks—0
STLight: a Fully Convolutional Approach for Efficient Predictive Learning by Spatio-Temporal joint Processing—0
ST-MambaSync: The Complement of Mamba and Transformers for Spatial-Temporal in Traffic Flow Prediction—0
ST-MLP: A Cascaded Spatio-Temporal Linear Framework with Channel-Independence Strategy for Traffic Forecasting—0
Stochastic Approximation Algorithms for Principal Component Analysis—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified