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 3171–3180 of 4891 papers

TitleStatusHype
Designing a Deep Learning-Driven Resource-Efficient Diagnostic System for Metastatic Breast Cancer: Reducing Long Delays of Clinical Diagnosis and Improving Patient Survival in Developing Countries—0
Deep Learning-based surrogate models for parametrized PDEs: handling geometric variability through graph neural networks—0
A Real-Time Robust Ecological-Adaptive Cruise Control Strategy for Battery Electric Vehicles—0
Unlocking the Potential of Similarity Matching: Scalability, Supervision and Pre-training—0
Novel Physics-Based Machine-Learning Models for Indoor Air Quality Approximations—0
WCCNet: Wavelet-integrated CNN with Crossmodal Rearranging Fusion for Fast Multispectral Pedestrian Detection—0
Best-Subset Selection in Generalized Linear Models: A Fast and Consistent Algorithm via Splicing Technique—0
DMFC-GraspNet: Differentiable Multi-Fingered Robotic Grasp Generation in Cluttered Scenes—0
Transport Equation based Physics Informed Neural Network to predict the Yield Strength of Architected Materials—0
Monaural Multi-Speaker Speech Separation Using Efficient Transformer Model—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified