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..
Dictionary Learning Based on Sparse Distribution Tomography
Authors: Pedram Pad, Farnood Salehi, Elisa Celis, Patrick Thiran, Michael Unser
ICML 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our algorithm by performing two types of experiments: image inpainting and image denoising. In both cases, we find that our approach is competitive with stateof-the-art dictionary learning techniques. |
| Researcher Affiliation | Academia | 1Biomedical Imaging Group, EPFL, Lausanne, Switzerland 2Computer Communications and Applications Laboratory 3, EPFL, Lausanne, Switzerland. |
| Pseudocode | Yes | The pseudocode of our dictionary learning method is given in Algorithm 1. |
| Open Source Code | No | The paper mentions using a 'Python package SPAMS' and provides its URL, but does not state that the code for the method described in *this* paper is open-source or provide a link to their own implementation. |
| Open Datasets | Yes | We use a database of face images provided by AT&T4 and crop them to have size 112 91 so we can chop each image to 208 patches of size 7 7, which correspond to yi in our model. [4] www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html |
| Dataset Splits | No | The paper describes how data is used for training and testing but does not explicitly mention a dedicated validation set or specific train/validation/test split percentages/counts. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'Python package SPAMS' but does not specify a version number for SPAMS or other software dependencies with their versions. |
| Experiment Setup | Yes | Algorithm 1 (Sparse DT) describes the initialization, iteration process, and adaptive step size parameters (η, κ+, κ-). It also mentions how the cost function E(B) is iteratively changed and how u vectors are regenerated randomly. |