On the Identifiability of Hybrid Deep Generative Models: Meta-Learning as a Solution
Authors: Yubo Ye, Maryam Tolou, Sumeet Vadhavkar, Xiajun Jiang, Huafeng Liu, Linwei Wang
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | On synthetic and real-data benchmarks, we provide strong empirical evidence for the un-identifiability of existing hybrid-DGMs using unconditional priors, and strong identifiability results of the presented meta-formulations of hybrid-DGMs. |
| Researcher Affiliation | Academia | Yubo Ye Zhejiang University 22230131@zju.edu.cn Maryam Toloubidokhti Rochester Institute of Technology mt6129@rit.edu Sumeet Vadhavkar Rochester Institute of Technology sv6234@rit.edu Xiajun Jiang Rochester Institute of Technology xj7056@rit.edu Huafeng Liu B Zhejiang University liuhf@zju.edu.cn Linwei Wang Rochester Institute of Technology linwei.wang@rit.edu |
| Pseudocode | No | The paper does not contain any pseudocode or explicitly labeled algorithm blocks. |
| Open Source Code | No | The data and code will be released soon. |
| Open Datasets | Yes | We considered three simulated and one real-world benchmarks for hybrid-DGMs, including three simulated physics systems of forced damped pendulum [2], advection-diffusion system [2] and double pendulum [4], and one real-world system double pendulum [4]. We used the dataset of a double pendulum introduced by [4]. |
| Dataset Splits | Yes | We generated total 60000 samples and separated them into a training, validation, and test sets with 4,0000, 10000, and 1,0000 samples, respectively. We generated 20000, 5000, and 5000 training, validation, and test samples, respectively. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory, or cloud instances) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | For each of the three simulated physics systems, we randomly sampled the initial states and parameters of the governing function to generate observations. We divided each video into observations of x s to consist of 20 temporal frames with a sampling frequency of 100 Hz. Because there is no clear indication of which samples belong to the same data-generation process, we use 7 samples preceding the current query sample as the context samples. We adopted Equation (19) as the physics-component of the hybrid-DGM, using the known lengths of the two arms (L1 = 91mm and L2 = 70mm) and assuming m = 1. |