Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
A General Efficient Hyperparameter-Free Algorithm for Convolutional Sparse Learning
Authors: Zheng Xu, Junzhou Huang
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments confirm the superior performance of our general algorithm in various convolutional sparse models, even better than some application-specialistic algorithms. |
| Researcher Affiliation | Academia | Zheng Xu, Junzhou Huang Department of Computer Science and Engineering The University of Texas at Arlington |
| Pseudocode | Yes | Algorithm 1 Primal-Dual Algorithm for Convolutional Sparsity Let ζ = m i=1 ki 2 1, L is a Lipschitz smooth constant of f. Choose β(0) X, μ(0) = (μ(0) 1 , μ(0) 2 , . . . , μ(0) m ) U = U1 U2 Um. Iterate: for t = 0, 1, 2, . . . Update primal variable: β(t+1) = prox g L+ζ β(t) 1/(L + ζ)[ f(β(t)) + CH [m]μ(t)] . Update dual variable: μ(t+1) = ΠB (μ(t) + (1/ζ)C[m](2β(t+1) β(t))). |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the described methodology. |
| Open Datasets | No | In this experiment, we hereby set X Rn p as a normal Gaussian matrix, and the smallest Lipschitz smooth constant of f(β) has been estimated as O(( n + p)2) according to (Rudelson and Vershynin 2010). β Rp is randomly generated as ground truth. ... We compare our method with SLEP (Liu, Ji, and Ye 2009)3, Chambolle & Pock (hereafter C & P) (Chambolle and Pock 2014), CVX (Grant and Boyd 2014) and f GFL (Xin et al. 2014). ... Joint Total Variation and Nuclear Norm Regularization ... It is written as the following optimization problems, min β 1 2 Xβ y 2 F + λ1 β + λ2 β 1. (14) Here, β has two spatial dimensions (i.e., a matrix), with X serving as a linear bounded subsampling operator. |
| Dataset Splits | No | The paper mentions numerical experiments but does not explicitly state the training, validation, or test dataset splits. |
| Hardware Specification | Yes | All the experiments in this section are conducted on a desktop computer with Intel Core i7-4770 CPU and 16 gigabyte RAM. |
| Software Dependencies | No | All methods are evaluated in MATLAB 2013b, Windows 7 Enterprise. |
| Experiment Setup | Yes | In this experiment, we hereby set X Rn p as a normal Gaussian matrix, and the smallest Lipschitz smooth constant of f(β) has been estimated as O(( n + p)2) according to (Rudelson and Vershynin 2010). β Rp is randomly generated as ground truth. We use the exact same parameter setting as in (Liu, Yuan, and Ye 2010), i.e., λ1 = 0.001, λ2 = 0.01. ... The parameter setting is λ1 = 100, λ2 = 1, following the same setting in (Huang et al. 2011). |