Generative Particle Variational Inference via Estimation of Functional Gradients
Authors: Neale Ratzlaff, Qinxun Bai, Li Fuxin, Wei Xu
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through carefully constructed experiments, we show that GPVI outperforms previous generative Par VI methods such as amortized SVGD, and is competitive with Par VI as well as gold-standard approaches like Hamiltonian Monte Carlo for fitting both exactly known and intractable target distributions. |
| Researcher Affiliation | Collaboration | 1Department of Electrical Engineering and Computer Science, Oregon State University, Corvallis, Oregon 2Horizon Robotics, Cupertino, California. |
| Pseudocode | Yes | Algorithm 1 Generative Particle VI (GPVI) |
| Open Source Code | No | The paper does not contain an explicit statement about releasing the source code or a link to a code repository. |
| Open Datasets | Yes | We evaluated on the MNIST and CIFAR-10 image datasets, following Neal et al. (2018) to split each dataset into 6 inlier classes and 4 outlier classes. |
| Dataset Splits | Yes | We evaluated on the MNIST and CIFAR-10 image datasets, following Neal et al. (2018) to split each dataset into 6 inlier classes and 4 outlier classes. We further split the dataset by only using the first six classes for training and testing. The remaining four classes are only used to compute the AUC and ECE statistics. |
| Hardware Specification | No | No specific hardware details (e.g., GPU models, CPU models, or memory specifications) used for running experiments were mentioned in the paper. |
| Software Dependencies | No | The paper mentions using the Adam optimizer but does not provide specific version numbers for any software dependencies like programming languages or libraries. |
| Experiment Setup | Yes | The details of our experimental setup are as follows. In our BNN experiments, we parameterized samples from the target distribution as neural networks with a fixed architecture. [...] For GPVI and amortized Par VI methods we used a 3 layer MLP hypernetwork with layer widths [256, 512, 1024], ReLU activations, and input noise z R256. We chose the Le Net-5 classifier architecture for all models, and trained for 100 epochs. |