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 4051–4060 of 4891 papers

TitleStatusHype
Deep Learning for Resilient Adversarial Decision Fusion in Byzantine Networks—0
Deep Learning for Simultaneous Inference of Hydraulic and Transport Properties—0
Deep Learning Improvements for Sparse Spatial Field Reconstruction—0
Deep Learning Meets Queue-Reactive: A Framework for Realistic Limit Order Book Simulation—0
Deep learning networks for selection of persistent scatterer pixels in multi-temporal SAR interferometric processing—0
Deep learning, stochastic gradient descent and diffusion maps—0
Deep Morphological Neural Networks—0
Deep Neural Network for Fast and Accurate Single Image Super-Resolution via Channel-Attention-based Fusion of Orientation-aware Features—0
Deep Neural Network Initialization with Sparsity Inducing Activations—0
Deep operator neural network applied to efficient computation of asteroid surface temperature and the Yarkovsky effect—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified