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 231–240 of 4891 papers

TitleStatusHype
SDS-Net: Shallow-Deep Synergism-detection Network for infrared small target detectionCode1
DAM: Dynamic Attention Mask for Long-Context Large Language Model Inference AccelerationCode1
DeePoly: A High-Order Accuracy Scientific Machine Learning Framework for Function Approximation and Solving PDEsCode1
Geo-Sign: Hyperbolic Contrastive Regularisation for Geometrically Aware Sign Language TranslationCode1
Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document EmbeddingsCode1
Efficient RAW Image Deblurring with Adaptive Frequency ModulationCode1
Neural Interpretable PDEs: Harmonizing Fourier Insights with Attention for Scalable and Interpretable Physics DiscoveryCode1
Taylor expansion-based Kolmogorov-Arnold network for blind image quality assessmentCode1
DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-ResolutionCode1
Decoupling Spatio-Temporal Prediction: When Lightweight Large Models Meet Adaptive HypergraphsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified