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..
Amortized Population Gibbs Samplers with Neural Sufficient Statistics
Authors: Hao Wu, Heiko Zimmermann, Eli Sennesh, Tuan Anh Le, Jan-Willem Van De Meent
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate APG samplers on three different tasks. |
| Researcher Affiliation | Academia | 1Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA 2Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA. |
| Pseudocode | Yes | Algorithm 1 Sequential Monte Carlo sampler... Algorithm 2 Amortized Population Gibbs Sampler |
| Open Source Code | No | The paper does not provide an explicit statement or link to publicly available source code for the methodology described. |
| Open Datasets | Yes | In the bouncing MNIST model, our data is a corpus of video frames that contain multiple moving MNIST digits. |
| Dataset Splits | No | The paper specifies training and testing corpora, but it does not explicitly mention a separate validation dataset split. |
| Hardware Specification | No | The paper mentions 'GPU memory' but does not provide specific details on the hardware used, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., specific Python libraries or frameworks). |
| Experiment Setup | Yes | We train with K = 5 sweeps, L = 10 particles, 20 instances per batch, learning rate 2.5 10 4, and 2 105 gradient steps. ... We train our model with K = 8 sweeps, L = 10 particles, 20 instances per batch, learning rate 10 4, and 3 105 gradient steps. ... with K = 5 sweeps, L = 10 particles, 5 instances per batch, learning rate 10 4, and 1.2 107 gradient steps. |