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 4251–4260 of 4891 papers

TitleStatusHype
Conditional Expectation Propagation—0
A Statistical Learning Approach to Reactive Power Control in Distribution Systems—0
Learning Mixtures of Plackett-Luce Models from Structured Partial OrdersCode0
CrevNet: Conditionally Reversible Video Prediction—0
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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified