Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows
Authors: Ruizhi Deng, Bo Chang, Marcus A. Brubaker, Greg Mori, Andreas Lehrmann
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we apply our models on synthetic data generated from common continuous-time stochastic processes and complex real-world datasets. The proposed CTFP and latent CTFP models are compared against two baseline models: latent ODEs [42] and variational RNNs (VRNNs) [12]. |
| Researcher Affiliation | Collaboration | Ruizhi Deng1,2 Bo Chang1 Marcus A. Brubaker1,3,4 Greg Mori1,2 Andreas M. Lehrmann1 1Borealis AI 2Simon Fraser University 3York University 4Vector Institute |
| Pseudocode | No | The paper presents mathematical formulations and descriptions of its model but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Please see the supplementary materials for details about our experimental setup and model implementations. |
| Open Datasets | Yes | Mujoco-Hopper [42] consists of 10,000 sequences that are simulated by a Hopper model from the Deep Mind Control Suite in a Mu Jo Co environment [46]. PTB Diagnostic Database (PTBDB) [4] consists of excerpts of ambulatory electrocardiography (ECG) recordings. Beijing Air-Quality Dataset (BAQD) [47] is a dataset consisting of multi-year recordings of weather and air quality data across different locations in Beijing. |
| Dataset Splits | No | The paper mentions 'training' and 'test' sets with specific parameters (e.g., 'λtrain = 2' and 'λtest = 20'), but it does not explicitly provide details about a separate 'validation' split or its size/methodology in the main text. |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running the experiments in the main text. |
| Software Dependencies | No | The paper mentions the use of GRU as the RNN cell but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | For latent ODE, latent CTFP, and VRNN, we report the (upper bound of) NLL estimated by the IWAE bound [6] in Equation 13, using K = 25 samples of latent variables. For CTFP, the reported values are exact; for the other three models, we report IWAE bounds using K = 125 samples. |