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..
Representation Learning of Compositional Data
Authors: Marta Avalos, Richard Nock, Cheng Soon Ong, Julien Rouar, Ke Sun
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on simulated data and microbiome data show the promise of our method. |
| Researcher Affiliation | Academia | Université de Bordeaux, Data61, the Australian National University and the University of Sydney first.last@{u-bordeaux.fr,data61.csiro.au} |
| Pseudocode | No | The paper does not contain any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source codes to reproduce our experimental results are available online2. 2https://bitbucket.org/Richard Nock/coda |
| Open Datasets | Yes | We consider the following datasets available in the microbiome R package [18], each of which is randomly split into a training set (90%) and a testing set (10%). The HITChip Atlas dataset [17] contains 130 genus-level taxonomic groups... The two-week diet swap study... was reported in [28]. |
| Dataset Splits | No | each of which is randomly split into a training set (90%) and a testing set (10%). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running experiments. |
| Software Dependencies | No | The paper mentions software like L-BFGS and ELU units, and the 'microbiome R package [18]', but does not provide specific version numbers for the software components used in their experiments. |
| Experiment Setup | Yes | the encoding map is modeled by a feed-forward neural network with two hidden layers of ELU [11] units, each of size 100. [...] Our implementation simply uses L-BFGS [9] based on the gradient of the loss. [...] Both clr-AE and Co DA-AE use exactly the same structure with one hidden layer of 100 ELU [11] units in their decoders. [...] we add a small random noise to the encoder input so as to avoid overfitting. |