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 2661–2670 of 4891 papers

TitleStatusHype
SAR Image Despeckling Using Quadratic-Linear Approximated L1-Norm—0
SAR Imaging of Moving Target based on Knowledge-aided Two-dimensional Autofocus—0
SatDiffMoE: A Mixture of Estimation Method for Satellite Image Super-resolution with Latent Diffusion Models—0
Scalable Co-Clustering for Large-Scale Data through Dynamic Partitioning and Hierarchical Merging—0
Scalable computation of prediction intervals for neural networks via matrix sketching—0
Scalable CP Decomposition for Tensor Learning using GPU Tensor Cores—0
Scalable Exploration for Neural Online Learning to Rank with Perturbed Feedback—0
Scalable Gaussian Processes with Low-Rank Deep Kernel Decomposition—0
Scalable Global Solution Techniques for High-Dimensional Models in Dynare—0
Scalable imputation of genetic data with a discrete fragmentation-coagulation process—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified