Independent mechanism analysis, a new concept?
Authors: Luigi Gresele, Julius von Kügelgen, Vincent Stimper, Bernhard Schölkopf, Michel Besserve
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide theoretical and empirical evidence that our approach circumvents a number of nonidentifiability issues arising in nonlinear blind source separation. (...) We experimentally validate our theoretical claims and propose a regularised maximum-likelihood learning approach based on the IMA constrast which outperforms the unregularised baseline ( 5). |
| Researcher Affiliation | Academia | 1 Max Planck Institute for Intelligent Systems, Tübingen, Germany 2 University of Cambridge |
| Pseudocode | No | The paper describes algorithms and models using mathematical equations and textual explanations, but it does not include any formal pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code available at: https://github.com/lgresele/independent-mechanism-analysis |
| Open Datasets | No | The paper uses synthetically generated data: "We sample the ground truth sources from a uniform distribution in [0, 1]n". It does not use a named, publicly available dataset. |
| Dataset Splits | No | The paper does not specify percentages or counts for training, validation, and test splits. It mentions generating "1000 random mixing functions" and "50 random mixings" for evaluation but not specific data splits. |
| Hardware Specification | No | The main text of the paper does not specify hardware details such as GPU/CPU models, memory, or cloud resources used for experiments. It mentions in the checklist that these are in Appendix E, which is not provided in the context. |
| Software Dependencies | No | In all of our experiments, we use JAX [12] and Distrax [13]. No specific version numbers for these libraries are provided in the main text. |
| Experiment Setup | Yes | Experimental setup. To use CIMA as a learning signal, we consider a regularised maximum-likelihood approach, with the following objective: L(g) = Ex[log pg(x)] λ CIMA(g 1, py), where g denotes the learnt unmixing, y = g(x) the reconstructed sources, and λ 0 a Lagrange multiplier. (...) We train a residual flow g (with full Jacobian) to maximise L. (...) Quantitative results for 50 learnt models for each λ {0.0, 0.5, 1.0} and n {5, 7} are summarised in Fig. 5. |