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..
Provable Subspace Identification Under Post-Nonlinear Mixtures
Authors: Qi Lyu, Xiao Fu
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | A series of numerical experiments corroborate our theoretical claims. |
| Researcher Affiliation | Academia | Qi Lyu School of EECS Oregon State University Corvallis, OR 97331 EMAIL Xiao Fu School of EECS Oregon State University Corvallis, OR 97331 EMAIL |
| Pseudocode | Yes | Algorithm 1: Post-Nonlinear Subspace Identification. |
| Open Source Code | Yes | The source code can be found online2. https://github.com/llvqi |
| Open Datasets | Yes | We use the human face electroencephalogram (EEG) dataset3. https://www.fil.ion.ucl.ac.uk/spm/data/mmfaces/ |
| Dataset Splits | Yes | We have N = 27, 692 samples of x , which are split as training, validation and test sets with 24794, 1449, and 1449 samples, respectively. |
| Hardware Specification | No | The paper does not specify the exact hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Adam optimizer [30]' but does not provide specific version numbers for Adam or any other software dependencies. |
| Experiment Setup | Yes | For the neural networks representing fm( ) and rm( ), we use R = 256 with Re LU activations. We use the Adam optimizer [30] with the initial learning rate being 2e 4 for the network optimization part. For the hyperparameters, we set λ = 1e 4. |