Multimeasurement Generative Models
Authors: Saeed Saremi, Rupesh Kumar Srivastava
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We study permutation invariant Gaussian M-densities on MNIST, CIFAR-10, and FFHQ-256 datasets, and demonstrate the effectiveness of this framework for realizing fast-mixing stable Markov chains in high dimensions. We present our experiments on MNIST, CIFAR-10, and FFHQ-256 datasets which were focused on permutation invariant M-densities. |
| Researcher Affiliation | Collaboration | Saeed Saremi1, 2& Rupesh Kumar Srivastava1 1NNAISENSE Inc. 2Redwood Center, UC Berkeley |
| Pseudocode | Yes | Algorithm 1: Walk-jump sampling (Saremi & Hyv arinen, 2019) using the discretization of Langevin diffusion by Sachs et al. (2017). Algorithm 2: Walk-Jump Sampling (WJS) using the discretization of Langevin diffusion from Lemma 1. |
| Open Source Code | Yes | Our code is publicly available at https://github.com/nnaisense/mems. |
| Open Datasets | Yes | Datasets Our experiments were conducted on the MNIST (Le Cun et al., 1998), CIFAR-10 (Krizhevsky, 2009) and FFHQ-256 (Karras et al., 2019; pre-processed version by Child, 2020) datasets. |
| Dataset Splits | No | The paper mentions using standard datasets (MNIST, CIFAR-10, FFHQ-256) and refers to 'training sets', but does not provide specific details on the train/validation/test splits (e.g., percentages, sample counts, or explicit references to predefined splits with full citations) needed for reproduction. |
| Hardware Specification | Yes | Table 2: Main hyperparameters and computational resources used for training MDAE models. ... GPUs 1 GTX Titan X 4 GTX Titan X 4 V100 |
| Software Dependencies | Yes | All models were implemented in the CPython (v3.8) library Py Torch v1.9.0 (Paszke et al., 2017) using the Py Torch Lightning framework v1.4.6 (Falcon et al.). |
| Experiment Setup | Yes | All important details of the experimental setup are provided in Appendix E. Table 2 lists the main hyperparameters used and hardware requirements for training MDAE models for each dataset. |