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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3621–3630 of 4891 papers

TitleStatusHype
ArrhythmiaVision: Resource-Conscious Deep Learning Models with Visual Explanations for ECG Arrhythmia Classification—0
Artificial Intelligence-Based Image Reconstruction in Cardiac Magnetic Resonance—0
Artificial neural networks condensation: A strategy to facilitate adaption of machine learning in medical settings by reducing computational burden—0
Artificial-Spiking Hierarchical Networks for Vision-Language Representation Learning—0
Artificial to Spiking Neural Networks Conversion for Scientific Machine Learning—0
A Scalable k-Medoids Clustering via Whale Optimization Algorithm—0
A Screening Strategy for Structured Optimization Involving Nonconvex _q,p Regularization—0
A Secure Estimator with Gaussian Bernoulli Mixture Model—0
A Selective Homomorphic Encryption Approach for Faster Privacy-Preserving Federated Learning—0
A Self-supervised Pressure Map human keypoint Detection Approch: Optimizing Generalization and Computational Efficiency Across Datasets—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified