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 4271–4280 of 4891 papers

TitleStatusHype
A Statistical Learning Approach to Reactive Power Control in Distribution Systems—0
Learning Mixtures of Plackett-Luce Models from Structured Partial OrdersCode0
ProLFA: Representative Prototype Selection for Local Feature AggregationCode0
Context-endcoding for neural network based skull stripping in magnetic resonance imaging—0
Iterative Distance-Aware Similarity Matrix Convolution with Mutual-Supervised Point Elimination for Efficient Point Cloud RegistrationCode0
Multiphase flow prediction with deep neural networks—0
SDCNet: Smoothed Dense-Convolution Network for Restoring Low-Dose Cerebral CT PerfusionCode0
Coping With Simulators That Don’t Always Return—0
Scale-Equivariant Steerable NetworksCode0
Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified