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 4091–4100 of 4891 papers

TitleStatusHype
Recurrent Relational Memory Network for Unsupervised Image Captioning—0
Accelerated Deep Reinforcement Learning Based Load Shedding for Emergency Voltage Control—0
Gradient-EM Bayesian Meta-learning—0
A deep convolutional neural network model for rapid prediction of fluvial flood inundation—0
Neural Architecture Optimization with Graph VAE—0
The Nyström method for convex loss functions—0
FREEtree: A Tree-based Approach for High Dimensional Longitudinal Data With Correlated FeaturesCode0
An Extended Integral Unit Commitment Formulation and an Iterative Algorithm for Convex Hull Pricing—0
Wasserstein Embedding for Graph LearningCode1
On sparse connectivity, adversarial robustness, and a novel model of the artificial neuron—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified