Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
A Nonconvex Projection Method for Robust PCA
Authors: Aritra Dutta, Filip Hanzely, Peter Richtàrik1468-1476
AAAI 2019 | Venue PDF | LLM Run Details | Input Tokens: 17,970 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,825 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate through extensive numerical experiments on a variety of applications, including shadow removal, background estimation, face detection, and galaxy evolution, that our approach matches and often significantly outperforms current state-of-the-art in various ways. |
| Researcher Affiliation | Academia | Aritra Dutta is with the Visual Computing Center, Division of Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) at King Abdullah University of Science and Technology, Thuwal, Saudi Arabia-23955-6900, e-mail: EMAIL. Filip Hanzely is with the Visual Computing Center, Division of Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) at King Abdullah University of Science and Technology, Thuwal, Saudi Arabia-23955-6900, e-mail: EMAIL. Peter Richt arik is with the Visual Computing Center, Division of Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) at King Abdullah University of Science and Technology, University of Edinburgh, and MIPT, e-mail: EMAIL. |
| Pseudocode | Yes | Algorithm 1: Alternating projection method for set feasibility |
| Open Source Code | No | The paper does not provide a link or explicit statement for the open-sourcing of its own code. |
| Open Datasets | Yes | We used the Basic sequence of the Stuttgart artificial dataset (Brutzer, H oferlin, and Heidemann 2012). |
| Dataset Splits | No | The paper describes generating synthetic data and using real-world datasets but does not explicitly provide details about train/validation/test splits or cross-validation for its experiments. |
| Hardware Specification | No | The paper does not provide specific details on the hardware used for running experiments. |
| Software Dependencies | No | The paper mentions using a 'fast implementation of n-th element computation from (Li 2013)' and sets parameters for algorithms (APG, i EALM, Go Dec) but does not specify any software names with version numbers. |
| Experiment Setup | Yes | For both APG and i EALM, we set λ = 1/ m and for i EALM we use µ = 1.25/ A 2 and ρ = 1.5, where A 2 is the spectral norm (maximum singular value) of A. For Go Dec we set q = 2. |