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 3171–3180 of 4891 papers

TitleStatusHype
Variance Reduced Median-of-Means Estimator for Byzantine-Robust Distributed Inference—0
Variational Approach for Efficient KL Divergence Estimation in Dirichlet Mixture Models—0
Variational Autoencoded Multivariate Spatial Fay-Herriot Models—0
Variational Autoencoders for Efficient Simulation-Based Inference—0
Variational Autoencoding of PDE Inverse Problems—0
Variational Bayesian Methods for a Tree-Structured Stick-Breaking Process Mixture of Gaussians by Application of the Bayes Codes for Context Tree Models—0
Variational Hamiltonian Monte Carlo via Score Matching—0
VeCAF: Vision-language Collaborative Active Finetuning with Training Objective Awareness—0
Vehicular Road Crack Detection with Deep Learning: A New Online Benchmark for Comprehensive Evaluation of Existing Algorithms—0
Verification of Neural Network Control Systems using Symbolic Zonotopes and Polynotopes—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified