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..
Momentum Particle Maximum Likelihood
Authors: Jen Ning Lim, Juan Kuntz, Samuel Power, Adam Michael Johansen
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section 6, we demonstrate empirically the efficacy of our method and study the effects of various design choices. As a large-scale experiment, we benchmark our proposed method for training Variational Autoencoders (Kingma and Welling, 2014) against current methods for training latent variable models. |
| Researcher Affiliation | Collaboration | 1University of Warwick 2Polygeist 3University of Bristol. |
| Pseudocode | Yes | Algorithm 1 A single subsampled step. In pink, we indicate the existing subsampling scheme of Kuntz et al. (2023). We indicate our proposed additions in teal. |
| Open Source Code | Yes | The code for reproducibility is available online: https://github.com/jenninglim/mpgd. |
| Open Datasets | Yes | For this task, we consider two datasets: MNIST (Le Cun et al., 1998) and CIFAR-10 (Krizhevsky and Hinton, 2009). |
| Dataset Splits | No | The paper states 'We use N = 5000 images for training and 3000 for testing' but does not explicitly mention or detail a validation split. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU models, or cloud computing instance types used for experiments. |
| Software Dependencies | No | The paper does not specify versions for any software dependencies or libraries used, such as Python, PyTorch, or CUDA. |
| Experiment Setup | Yes | MPD. For step sizes, we have hθ = hx = 10 4. The number of particles is set to M = 5. For the momentum parameters, we use γθ = γx = 0.9 with the momentum coefficient µθ = 0.95, µx = 0 (or equivalently, ηθ 556, ηx 11, 111 )for MNIST and µθ = 0.95, µx = 0.5 (or equivalently, ηθ 556, , ηx 55, 555 ) for CIFAR. We use the RMSProp preconditioner (see Appendix J.1) with β = 0.9. |