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
Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive UncertaintiesCode0
Optimal Distributed Subsampling for Maximum Quasi-Likelihood Estimators with Massive Data—0
MOTS: Multiple Object Tracking for General Categories Based On Few-Shot Method—0
An Efficient Machine-Learning Approach for PDF Tabulation in Turbulent Combustion Closure—0
Improved Protein-ligand Binding Affinity Prediction with Structure-Based Deep Fusion InferenceCode1
Speech to Text Adaptation: Towards an Efficient Cross-Modal Distillation—0
Convolutional Neural Network for emotion recognition to assist psychiatrists and psychologists during the COVID-19 pandemic: experts opinion—0
Visual Perception Model for Rapid and Adaptive Low-light Image Enhancement—0
Adaptive Transformers for Learning Multimodal RepresentationsCode1
Walking with Perception: Efficient Random Walk Sampling via Common Neighbor Awareness—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified