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 3661–3670 of 4891 papers

TitleStatusHype
Robust Peak Detection for Holter ECGs by Self-Organized Operational Neural NetworksCode1
Equivariant Transformers for Neural Network based Molecular Potentials—0
Why does Negative Sampling not Work Well? Analysis of Convexity in Negative Sampling—0
Understanding the Variance Collapse of SVGD in High Dimensions—0
MAGNEx: A Model Agnostic Global Neural Explainer—0
A NEW BACKBONE FOR HYPERSPECTRAL IMAGE RECONSTRUCTION—0
Transformer-based Transform CodingCode1
SUMNAS: Supernet with Unbiased Meta-Features for Neural Architecture Search—0
L-SR1 Adaptive Regularization by Cubics for Deep Learning—0
An Attention-LSTM Hybrid Model for the Coordinated Routing of Multiple Vehicles—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified