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 4121–4130 of 4891 papers

TitleStatusHype
A Computational Separation between Private Learning and Online Learning—0
Optimization from Structured Samples for Coverage Functions—0
Meta-Learning Divergences of Variational Inference—0
Local Grid Rendering Networks for 3D Object Detection in Point Clouds—0
High Dimensional Bayesian Optimization Assisted by Principal Component AnalysisCode0
Progressive Tandem Learning for Pattern Recognition with Deep Spiking Neural Networks—0
End-to-End JPEG Decoding and Artifacts Suppression Using Heterogeneous Residual Convolutional Neural Network—0
Go Wide, Then Narrow: Efficient Training of Deep Thin Networks—0
Acoustic Source Localization with the Angular Spectrum Approach in Continuously Stratified Media—0
Statistical Mechanical Analysis of Neural Network PruningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified