Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Multimeasurement Generative Models
Authors: Saeed Saremi, Rupesh Kumar Srivastava
ICLR 2022 | Venue PDF | 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. |