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 4801–4825 of 4891 papers

TitleStatusHype
Scalable Nonlinear Learning with Adaptive Polynomial Expansions—0
Backhaul-Constrained Multi-Cell Cooperation Leveraging Sparsity and Spectral Clustering—0
Extracting man-made objects from remote sensing images via fast level set evolutions—0
An inexact Newton-Krylov algorithm for constrained diffeomorphic image registration—0
Improved Distributed Principal Component Analysis—0
Statistical and computational trade-offs in estimation of sparse principal components—0
Bi-l0-l2-Norm Regularization for Blind Motion Deblurring—0
Object Proposal Generation using Two-Stage Cascade SVMs—0
Generalized Higher-Order Tensor Decomposition via Parallel ADMM—0
Sparse Estimation with the Swept Approximated Message-Passing AlgorithmCode0
Bayesian Optimal Control of Smoothly Parameterized Systems: The Lazy Posterior Sampling Algorithm—0
Semantic Graph for Zero-Shot Learning—0
Eigenspace Method for Spatiotemporal Hotspot Detection—0
Algebraic-Combinatorial Methods for Low-Rank Matrix Completion with Application to Athletic Performance Prediction—0
Shrinkage Fields for Effective Image Restoration—0
Efficient Computation of Relative Pose for Multi-Camera Systems—0
Inference of Sparse Networks with Unobserved Variables. Application to Gene Regulatory NetworksCode0
Off-Policy Shaping Ensembles in Reinforcement Learning—0
Single camera pose estimation using Bayesian filtering and Kinect motion priorsCode0
Fast Ridge Regression with Randomized Principal Component Analysis and Gradient Descent—0
A Rank-SVM Approach to Anomaly Detection—0
Bayesian Neural Networks for Genetic Association Studies of Complex DiseaseCode0
Distribution-Aware Sampling and Weighted Model Counting for SAT—0
Don't Fall for Tuning Parameters: Tuning-Free Variable Selection in High Dimensions With the TREX—0
Correlation Filters with Limited Boundaries—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified