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..
Efficient Gradient-Based Inference through Transformations between Bayes Nets and Neural Nets
Authors: Diederik Kingma, Max Welling
ICML 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Theoretical results are supported by experiments. (Abstract), Experimental results are shown in section 6. (Section 1.1), We applied a Hybrid Monte Carlo (HMC) sampler to a Dynamic Bayesian Network (DBN)... (Section 6.1), trained on a small (1000 datapoints) and large (50000 datapoints) version of the MNIST dataset. (Section 6.2) |
| Researcher Affiliation | Academia | Diederik P. Kingma EMAIL Max Welling EMAIL Machine Learning Group, University of Amsterdam |
| Pseudocode | No | No pseudocode or algorithm blocks found. |
| Open Source Code | No | No explicit statement or link for open-source code for the described methodology was found. |
| Open Datasets | Yes | The model was trained on a small (1000 datapoints) and large (50000 datapoints) version of the MNIST dataset. (Section 6.2) |
| Dataset Splits | No | The paper mentions training on MNIST but does not provide specific details on train/validation/test splits, percentages, or sample counts for these splits. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory, or cloud instance types) used for running the experiments are provided. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., library names, frameworks, or solvers with their versions) are provided. |
| Experiment Setup | Yes | For HMC we used 10 leapfrog steps per sample, and the stepsize was automatically adjusted while sampling to obtain a HMC acceptance rate of around 0.9. At each sampling run, the ο¬rst 1000 HMC samples were thrown away (burn-in); the subsequent 4000 HMC samples were kept. (Section 6.1), For MCEM, we used HMC with 10 leapfrog steps followed by a weight update using Adagrad (Duchi et al., 2010). For MMCL, we used L {10, 100, 500}. (Section 6.2) |