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 4411–4420 of 4891 papers

TitleStatusHype
Sparse Least Squares Low Rank Kernel Machines—0
DeGraF-Flow: Extending DeGraF Features for accurate and efficient sparse-to-dense optical flow estimation—0
ICLabel: An automated electroencephalographic independent component classifier, dataset, and websiteCode0
Efficient surrogate modeling methods for large-scale Earth system models based on machine learning techniques—0
Robust and Adaptive Planning under Model Uncertainty—0
Stochastic Approximation Algorithms for Principal Component Analysis—0
Reproducibility Evaluation of SLANT Whole Brain Segmentation Across Clinical Magnetic Resonance Imaging Protocols—0
Self-Learning Exploration and Mapping for Mobile Robots via Deep Reinforcement LearningCode0
Efficient Convolutional Neural Network Training with Direct Feedback Alignment—0
Artificial neural networks condensation: A strategy to facilitate adaption of machine learning in medical settings by reducing computational burden—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified