Identifying through Flows for Recovering Latent Representations
Authors: Shen Li, Bryan Hooi, Gim Hee Lee
ICLR 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Simulations on synthetic data validate the correctness and effectiveness of our proposed method and demonstrate its practical advantages over other existing methods. |
| Researcher Affiliation | Academia | Shen Li Institute of Data Science & NUS Graduate School for Integrative Sciences and Engineering National University of Singapore shen.li@u.nus.edu Bryan Hooi & Gim Hee Lee Department of Computer Science, National University of Singapore {bhooi,gimhee.lee}@comp.nus.edu.sg |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper provides a link to the code for a baseline model (iVAE) used for comparison, but no explicit statement or link for the open-source code of their proposed iFlow method. |
| Open Datasets | No | We generate a synthetic dataset where the sources are non-stationary Gaussian time-series, as described in (Khemakhem et al., 2019): the sources are divided into M segments of L samples each. The auxiliary variable u is set to be the segment index. The paper describes how the synthetic data is generated but does not provide a direct link or citation for accessing the specific generated dataset used in their experiments. |
| Dataset Splits | No | The paper describes the generation of a synthetic dataset and uses mini-batches for training, but does not explicitly provide specific training/test/validation dataset splits (e.g., percentages, sample counts, or specific split files). |
| Hardware Specification | No | No specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments were found. |
| Software Dependencies | No | The paper mentions using an 'Adam optimizer' and 'RQ-NSF(AR)' for model architecture, but does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions, or specific library versions). |
| Experiment Setup | Yes | The mapping λθ that outputs the natural parameters of the conditional factorized exponential family distribution is parameterized by a multi-layer perceptron with the activation of the last layer being the softplus nonlinearity. Additionally, a negative activation is taken on the second-order natural parameters in order to ensure its finiteness. The bijection hφ is modeled by RQ-NSF(AR) (Durkan et al., 2019b) with the flow length of 10 and the bin 8, which gives rise to sufficient flexibility and expressiveness. For each training iteration, we use a mini-batch of size 64, and an Adam optimizer with learning rate chosen in {0.01, 0.001} to optimize the learning objective (15). |