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 851–860 of 4891 papers

TitleStatusHype
Efficient Transformations in Deep Learning Convolutional Neural Networks—0
MoiréXNet: Adaptive Multi-Scale Demoiréing with Linear Attention Test-Time Training and Truncated Flow Matching Prior—0
Efficient Malware Detection with Optimized Learning on High-Dimensional Features—0
Brain Stroke Classification Using Wavelet Transform and MLP Neural Networks on DWI MRI Images—0
Enhancing One-run Privacy Auditing with Quantile Regression-Based Membership Inference—0
Islanding Strategy for Smart Grids Oriented to Resilience Enhancement and Its Power Supply Range Optimization—0
Accurate and scalable exchange-correlation with deep learning—0
sHGCN: Simplified hyperbolic graph convolutional neural networksCode0
Expressive Score-Based Priors for Distribution Matching with Geometry-Preserving RegularizationCode0
Swarm-STL: A Framework for Motion Planning in Large-Scale, Multi-Swarm Systems—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified