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 3951–3960 of 4891 papers

TitleStatusHype
Continual Learning via Online Leverage Score Sampling—0
Continuous Normalizing Flows for Uncertainty-Aware Human Pose Estimation—0
Contract-based hierarchical control using predictive feasibility value functions—0
Convex Reformulation of Information Constrained Linear State Estimation with Mixed-Binary Variables for Outlier Accommodation—0
ConvNeXt-ChARM: ConvNeXt-based Transform for Efficient Neural Image Compression—0
Convolutional Spiking Neural Networks for Detecting Anticipatory Brain Potentials Using Electroencephalogram—0
ConvTimeNet: A Pre-trained Deep Convolutional Neural Network for Time Series Classification—0
Cooperative Bistatic ISAC Systems for Low-Altitude Economy—0
Optimizing Cooperative path-finding: A Scalable Multi-Agent RRT* with Dynamic Potential Fields—0
Coordination of OLTC and Smart Inverters for Optimal Voltage Regulation of Unbalanced Distribution Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified