Multiscale Invertible Generative Networks for High-Dimensional Bayesian Inference
Authors: Shumao Zhang, Pengchuan Zhang, Thomas Y Hou
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We study two high-d Bayesian inverse problems (BIPs) in Section 6.1 as test beds for distribution approximation and multi-mode capture. We also apply Ms IGN to the image synthesis task to benchmark with flow-based generative models and demonstrate its interpret-ability in Section 6.2. |
| Researcher Affiliation | Collaboration | 1Department of Computational & Mathematical Sciences, Caltech, Pasadena, California, USA 2MSR AI Lab, Redmond, Washington, USA. |
| Pseudocode | Yes | Algorithm 1 Train Ms IGN by optimizing the Jeffreys divergence in a multi-stage manner |
| Open Source Code | No | The paper does not contain any explicit statement about making the source code available or provide a link to a code repository. |
| Open Datasets | Yes | Table 2. Bits-per-dimension value comparison with baseline models of flow-based generative networks. All models in this table do not use the variational dequantization technique in (Ho et al., 2019). *: Score obtained by our own reproducing experiment. MODEL MNIST CIFAR-10 CELEBA 64 IMAGENET 32 IMAGENET 64 |
| Dataset Splits | No | The paper mentions using datasets like MNIST, CIFAR-10, CelebA, and ImageNet for image synthesis, but it does not specify the exact training, validation, and test split percentages or sample counts in the provided text. It refers to Appendix F for more details, which is not available. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper refers to using 'the invertible block of Glow (Kingma & Dhariwal, 2018)' and mentions 'Adam' for optimization, but it does not specify version numbers for any software dependencies or libraries. |
| Experiment Setup | No | The paper states high-level architectural details like 'L = 6 scales' and 'average pooling with kernel size 2 and stride 2' and mentions referring to Appendix F for 'More details of experimental setting', but does not explicitly provide specific hyperparameter values (e.g., learning rate, batch size, epochs) in the main text. |