oi-VAE: Output Interpretable VAEs for Nonlinear Group Factor Analysis
Authors: Samuel K. Ainsworth, Nicholas J. Foti, Adrian K. C. Lee, Emily B. Fox
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the oi-VAE on motion capture and magnetoencephalography datasets. In these scenarios where there is a natural notion of groupings of observations, we demonstrate the interpretability of the learned features and how these structures of interaction correspond to physically meaningful systems. Furthermore, in such cases we show that the regularization employed by oi-VAE leads to better generalization and synthesis capabilities, especially in limited training data scenarios or when the training data might not fully capture the observed space of interest. |
| Researcher Affiliation | Academia | 1Paul G. Allen School of Computer Science and Engineering, University of Washington 2Institute for Learning & Brain Sciences and Department of Speech and Hearing Sciences, University of Washington. |
| Pseudocode | Yes | Algorithm 1 Collapsed VI for oi-VAE |
| Open Source Code | No | The paper does not provide a direct link or explicit statement about the availability of the open-source code for the methodology described. |
| Open Datasets | Yes | Using data collected from CMU s motion capture database we evaluated oi-VAE s ability to handle complex physical constraints and interactions across groups of joint angles while simultaneously identifying a sparse decomposition of human motion. We apply our oi-VAE method to infer low-rank representations of source-space MEG data where the groups are specified as the 40 regions defined in the HCP-MMP1 brain parcellation (Glasser et al., 2016). |
| Dataset Splits | No | The paper mentions training and testing on held-out examples but does not explicitly specify a separate 'validation' dataset split for hyperparameter tuning or early stopping, which is typically required for reproducibility of the validation phase. |
| Hardware Specification | No | The paper acknowledges "the support of NVIDIA Corporation for the donated GPU used for this research," but it does not specify the exact model or details of the GPU or any other hardware components like CPU, RAM, or specific cluster configurations. |
| Software Dependencies | No | The paper does not specify version numbers for any key software components or libraries used in the experiments (e.g., Python version, specific deep learning frameworks like TensorFlow or PyTorch, or scientific computing libraries). |
| Experiment Setup | Yes | In all experiments, we use λ = 1. To explore how the learned disentangled latent representation varies with latent dimension K, we use K = 4, 8, and 16. We applied oi-VAE with K = 20, λ = 10, and Alg. 1 for 10, 000 iterations. |