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

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Showing 4826–4850 of 4891 papers

TitleStatusHype
Matroid Bandits: Fast Combinatorial Optimization with Learning—0
Data-driven HRF estimation for encoding and decoding models—0
Binary Stereo MatchingCode0
A high-reproducibility and high-accuracy method for automated topic classification—0
Approximate Model-Based Diagnosis Using Greedy Stochastic Search—0
Smart machines and the SP theory of intelligence—0
The return of AdaBoost.MH: multi-class Hamming trees—0
Parallelizing MCMC via Weierstrass SamplerCode0
Reconciling "priors" & "priors" without prejudice?—0
Bayesian inference for low rank spatiotemporal neural receptive fields—0
A* Lasso for Learning a Sparse Bayesian Network Structure for Continuous Variables—0
Global Solver and Its Efficient Approximation for Variational Bayesian Low-rank Subspace Clustering—0
Simultaneous Rectification and Alignment via Robust Recovery of Low-rank Tensors—0
Factorized Asymptotic Bayesian Inference for Latent Feature Models—0
Learning Pairwise Graphical Models with Nonlinear Sufficient Statistics—0
Fast large-scale optimization by unifying stochastic gradient and quasi-Newton methodsCode0
Efficient State-Space Inference of Periodic Latent Force Models—0
A Robust Variational Model for Positive Image Deconvolution—0
MINT: Mutual Information based Transductive Feature Selection for Genetic Trait Prediction—0
Electricity Market Forecasting via Low-Rank Multi-Kernel Learning—0
Cross-Recurrence Quantification Analysis of Categorical and Continuous Time Series: an R package—0
Context-aware recommendations from implicit data via scalable tensor factorization—0
Feature Extraction of Hyperspectral Images With Image Fusion and Recursive Filtering—0
Online Tensor Methods for Learning Latent Variable ModelsCode0
DeBaCl: A Python Package for Interactive DEnsity-BAsed CLusteringCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified