Representation Learning of Compositional Data
Authors: Marta Avalos, Richard Nock, Cheng Soon Ong, Julien Rouar, Ke Sun
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | 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. |