BasisDeVAE: Interpretable Simultaneous Dimensionality Reduction and Feature-Level Clustering with Derivative-Based Variational Autoencoders
Authors: Dominic Danks, Christopher Yau
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the performance and scalability of the De VAE and Basis De VAE models on synthetic and real-world data and present how the derivative-based approach allows for expressive yet interpretable forward models which respect prior knowledge. 5. Experiments |
| Researcher Affiliation | Academia | 1Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK 2The Alan Turing Institute, London, UK 3Division of Informatics, Imaging & Data Sciences, Unversity of Manchester, Manchester, UK 4Health Data Research UK, London, UK. |
| Pseudocode | No | The paper does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | We provide a GPU-aware Py Torch (Paszke et al., 2019) implementation of our approach at https: //github.com/djdanks/Basis De VAE and demonstrate its application to multiple settings in Section 5. |
| Open Datasets | Yes | We use OASIS-3 (La Montagne et al., 2019) which is the latest iteration of released data and contains entries from over 2,000 MRI sessions of patients at various stages of cognitive decline. analysing a single-cell RNA sequencing (sc RNA-seq) mouse spermatogenesis dataset (Ernst et al., 2019) |
| Dataset Splits | No | Each iteration consists of i) randomly partitioning the data into an 80%/20% train/test split, ii) training on the training data and iii) evaluating two metrics on the test data. We train on a randomly sampled 90% portion of the data and reserve the remaining 10% for test-set evaluation. No explicit validation set is mentioned for either experiment. |
| Hardware Specification | Yes | All computations were performed on a Linux (Ubuntu) desktop with an Intel i7-4790K 4GHz CPU and NVIDIA GTX 980 GPU (4GB VRAM). |
| Software Dependencies | No | The paper mentions using 'Py Torch' but does not provide a specific version number for it or any other key software dependencies. |
| Experiment Setup | Yes | Each model is trained for 50 epochs using Adam (Kingma & Ba, 2015) with a 5 10 3 learning rate and employs a Gaussian conditional log-likelihood in the decoder. In both models we use β = 10, γ = 1, α = 0.1 and optimise using Adam with a 5 10 3 learning rate. We apply linear KL-annealing (Bowman et al., 2016b) to (β, γ) over the first 20% of 100 training epochs. |