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..
Approximating Orthogonal Matrices with Effective Givens Factorization
Authors: Thomas Frerix, Joan Bruna
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate numerical results of approximating the graph Fourier transform... |
| Researcher Affiliation | Academia | 1Technical University of Munich 2New York University. |
| Pseudocode | Yes | Algorithm 1 Coordinate descent on the L1-criterion |
| Open Source Code | Yes | An implementation of these algorithms can be found at https://github.com/tfrerix/ givens-factorization |
| Open Datasets | Yes | MINNESOTA 2642 3304 (Defferrard et al.) HUMANPROTEIN 3133 6726 (Rual et al., 2005) EMAIL 1133 5451 (Guimer a et al., 2003) FACEBOOK 2888 2981 (Mc Auley & Leskovec, 2012) |
| Dataset Splits | No | The paper describes the generation of datasets (planted models, Barabasi-Albert graphs, and uses real-world graph datasets) but does not specify any train/validation/test splits, percentages, or explicit methodologies for partitioning data. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory, cloud instances) used for running the experiments. |
| Software Dependencies | No | While an implementation link is provided, the paper does not explicitly list software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x) within its text. |
| Experiment Setup | Yes | To obtain a Givens sequence, we factorize these samples with manifold coordinate descent on the L1-objective (13). Along the optimization path, we define Nϵ(U) as the number of Givens factors for which the normalized approximation error (3) is smaller than ϵ = 0.1, i.e., Nϵ(U) := min N ||U − G1 . . . GN ||F,sym < ϵ |