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 1291–1300 of 4891 papers

TitleStatusHype
An Enhancement of CNN Algorithm for Rice Leaf Disease Image Classification in Mobile Applications—0
Automatic Doubly Robust Forests—0
Deep Learning-Enhanced Preconditioning for Efficient Conjugate Gradient Solvers in Large-Scale PDE Systems—0
Tazza: Shuffling Neural Network Parameters for Secure and Private Federated Learning—0
PrisonBreak: Jailbreaking Large Language Models with Fewer Than Twenty-Five Targeted Bit-flips—0
CrackESS: A Self-Prompting Crack Segmentation System for Edge Devices—0
Multi-Behavior Recommendation with Personalized Directed Acyclic Behavior GraphsCode1
Compression for Better: A General and Stable Lossless Compression Framework—0
Advancing clinical trial outcomes using deep learning and predictive modelling: bridging precision medicine and patient-centered care—0
Digital Twin-Empowered Voltage Control for Power Systems—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified