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 3791–3800 of 4891 papers

TitleStatusHype
Self-Supervised Learning of Perceptually Optimized Block Motion Estimates for Video Compression—0
A new weakly supervised approach for ALS point cloud semantic segmentation—0
Learn to Communicate with Neural Calibration: Scalability and Generalization—0
Understanding the Variance Collapse of SVGD in High Dimensions—0
SUMNAS: Supernet with Unbiased Meta-Features for Neural Architecture Search—0
An Attention-LSTM Hybrid Model for the Coordinated Routing of Multiple Vehicles—0
Why does Negative Sampling not Work Well? Analysis of Convexity in Negative Sampling—0
Equivariant Transformers for Neural Network based Molecular Potentials—0
A NEW BACKBONE FOR HYPERSPECTRAL IMAGE RECONSTRUCTION—0
MAGNEx: A Model Agnostic Global Neural Explainer—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified