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 3461–3470 of 4891 papers

TitleStatusHype
Deep fiber clustering: Anatomically informed fiber clustering with self-supervised deep learning for fast and effective tractography parcellationCode1
RANG: A Residual-based Adaptive Node Generation Method for Physics-Informed Neural NetworksCode1
Cluster-based Regression using Variational Inference and Applications in Financial Forecasting—0
Novel optimized crow search algorithm for feature selectionCode0
Ultra Fast Speech Separation Model with Teacher Student Learning—0
SwinFuse: A Residual Swin Transformer Fusion Network for Infrared and Visible ImagesCode1
Adversarial Contrastive Learning by Permuting Cluster Assignments—0
Toward Fast, Flexible, and Robust Low-Light Image EnhancementCode2
Working memory inspired hierarchical video decomposition with transformative representationsCode0
Proximal Implicit ODE Solvers for Accelerating Learning Neural ODEs—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified