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 4776–4800 of 4891 papers

TitleStatusHype
Incorporating Word Correlation Knowledge into Topic Modeling—0
Local, Private, Efficient Protocols for Succinct Histograms—0
Kernel Manifold AlignmentCode0
New Routes to Phylogeography—0
On learning optimized reaction diffusion processes for effective image restorationCode0
A Chaining Algorithm for Online Nonparametric Regression—0
On the consistency theory of high dimensional variable screening—0
Curvilinear-Coordinate-Based Object and Situation Assessment for Highly Automate—0
Dependent Matérn Processes for Multivariate Time Series—0
Learning Efficient Anomaly Detectors from K-NN Graphs—0
Computational and Statistical Boundaries for Submatrix Localization in a Large Noisy Matrix—0
maxDNN: An Efficient Convolution Kernel for Deep Learning with Maxwell GPUsCode0
Sparse Distance Weighted Discrimination—0
Sketch and Validate for Big Data Clustering—0
Stochastic Local Interaction (SLI) Model: Interfacing Machine Learning and Geostatistics—0
Minimizing the Number of Matching Queries for Object Retrieval—0
Fixed Point Algorithm Based on Quasi-Newton Method for Convex Minimization Problem with Application to Image Deblurring—0
Iterative Subsampling in Solution Path Clustering of Noisy Big Data—0
On Sparse Gaussian Chain Graph Models—0
Scalable Non-linear Learning with Adaptive Polynomial Expansions—0
An Integer Polynomial Programming Based Framework for Lifted MAP Inference—0
Higher-order MRFs based image super resolution: why not MAP?—0
Variational Reformulation of Bayesian Inverse ProblemsCode0
Recovery of Sparse Signals Using Multiple Orthogonal Least Squares—0
On the Computational Efficiency of Training Neural NetworksCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified