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
HAAQI-Net: A Non-intrusive Neural Music Audio Quality Assessment Model for Hearing AidsCode1
Automating DBSCAN via Deep Reinforcement LearningCode1
DeepSeeColor: Realtime Adaptive Color Correction for Autonomous Underwater Vehicles via Deep Learning MethodsCode1
HADAS: Hardware-Aware Dynamic Neural Architecture Search for Edge Performance ScalingCode1
A Control-Oriented Simplified Single Particle Model with Grouped Parameter and Sensitivity Analysis for Lithium-Ion BatteriesCode1
DAM: Dynamic Attention Mask for Long-Context Large Language Model Inference AccelerationCode1
Learning Enriched Features via Selective State Spaces Model for Efficient Image DeblurringCode1
Highly accurate and efficient deep learning paradigm for full-atom protein loop modeling with KarmaLoopCode1
DASS: Distilled Audio State Space Models Are Stronger and More Duration-Scalable LearnersCode1
Cross-Modality Multi-Atlas Segmentation via Deep Registration and Label FusionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified