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 4851–4875 of 4891 papers

TitleStatusHype
Levels of Integration between Low-Level Reasoning and Task Planning—0
Safe Screening With Variational Inequalities and Its Application to LASSO—0
A Statistical Perspective on Algorithmic Leveraging—0
P-HGRMS: A Parallel Hypergraph Based Root Mean Square Algorithm for Image Denoising—0
Efficient Classification for Metric Data—0
Patch Match Filter: Efficient Edge-Aware Filtering Meets Randomized Search for Fast Correspondence Field Estimation—0
Sampling Strategies for Real-Time Action Recognition—0
Discrete MRF Inference of Marginal Densities for Non-uniformly Discretized Variable Space—0
A Fast Approximate AIB Algorithm for Distributional Word Clustering—0
Fast Rigid Motion Segmentation via Incrementally-Complex Local Models—0
Efficient Detector Adaptation for Object Detection in a Video—0
High-dimensional Mixed Graphical Models—0
Efficient Distance Metric Learning by Adaptive Sampling and Mini-Batch Stochastic Gradient Descent (SGD)—0
Computational Lower Bounds for Sparse PCA—0
Utility-Based Control for Computer Vision—0
Video Face Matching using Subset Selection and Clustering of Probabilistic Multi-Region Histograms—0
Fast Matching by 2 Lines of Code for Large Scale Face Recognition Systems—0
A Comparison of Lauritzen-Spiegelhalter, Hugin, and Shenoy-Shafer Architectures for Computing Marginals of Probability DistributionsCode0
Sparse/Robust Estimation and Kalman Smoothing with Nonsmooth Log-Concave Densities: Modeling, Computation, and Theory—0
Why Size Matters: Feature Coding as Nystrom Sampling—0
Semi-Crowdsourced Clustering: Generalizing Crowd Labeling by Robust Distance Metric Learning—0
Scalable imputation of genetic data with a discrete fragmentation-coagulation process—0
Sparse Approximate Manifolds for Differential Geometric MCMC—0
A Marginalized Particle Gaussian Process Regression—0
Smoothing Dynamic Systems with State-Dependent Covariance Matrices—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified