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 651–660 of 4891 papers

TitleStatusHype
DeepSeeColor: Realtime Adaptive Color Correction for Autonomous Underwater Vehicles via Deep Learning MethodsCode1
Automating DBSCAN via Deep Reinforcement LearningCode1
How Much Can Time-related Features Enhance Time Series Forecasting?Code1
MGStream: Motion-aware 3D Gaussian for Streamable Dynamic Scene ReconstructionCode1
Improving Facade Parsing with Vision Transformers and Line IntegrationCode1
DeepZero: Scaling up Zeroth-Order Optimization for Deep Model TrainingCode1
DeformUX-Net: Exploring a 3D Foundation Backbone for Medical Image Segmentation with Depthwise Deformable ConvolutionCode1
Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical AnalysisCode1
A Control-Oriented Simplified Single Particle Model with Grouped Parameter and Sensitivity Analysis for Lithium-Ion BatteriesCode1
Learning Enriched Features via Selective State Spaces Model for Efficient Image DeblurringCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified