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 641–650 of 4891 papers

TitleStatusHype
Deep Transfer Learning for Land Use and Land Cover Classification: A Comparative StudyCode1
DnS: Distill-and-Select for Efficient and Accurate Video Indexing and RetrievalCode1
A Control-Oriented Simplified Single Particle Model with Grouped Parameter and Sensitivity Analysis for Lithium-Ion BatteriesCode1
GraphMamba: An Efficient Graph Structure Learning Vision Mamba for Hyperspectral Image ClassificationCode1
DAM: Dynamic Attention Mask for Long-Context Large Language Model Inference AccelerationCode1
Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical AnalysisCode1
GS-I^3: Gaussian Splatting for Surface Reconstruction from Illumination-Inconsistent ImagesCode1
Guidelines and Evaluation of Clinical Explainable AI in Medical Image AnalysisCode1
M3-Jepa: Multimodal Alignment via Multi-directional MoE based on the JEPA frameworkCode1
Learning Enriched Features via Selective State Spaces Model for Efficient Image DeblurringCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified