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 [1].
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. |