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..
Emerging Convolutions for Generative Normalizing Flows
Authors: Emiel Hoogeboom, Rianne Van Den Berg, Max Welling
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show that the flexibility of d d convolutions significantly improves the performance of generative flow models on galaxy images, CIFAR10 and Image Net. |
| Researcher Affiliation | Academia | 1Uv A-Bosch Delta Lab, University of Amsterdam, Netherlands 2University of Amsterdam, Netherlands 3Canadian Institute for Advanced Research (CIFAR). Correspondence to: Emiel Hoogeboom <EMAIL>. |
| Pseudocode | No | The paper describes methods in text and equations but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is available at: github.com/ehoogeboom/emerging. |
| Open Datasets | Yes | CIFAR10 (Krizhevsky & Hinton, 2009) and Image Net (Russakovsky et al., 2015). |
| Dataset Splits | No | The paper mentions using datasets like CIFAR10 and ImageNet, but does not explicitly provide details on train/validation/test splits (e.g., percentages, sample counts, or explicit reference to a specific standard split used) to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (like GPU/CPU models or specific machine configurations) used for running its experiments. |
| Software Dependencies | No | The paper mentions software like Tensorflow and Cython, but does not provide specific version numbers for these or any other ancillary software components used in the experiments. |
| Experiment Setup | No | The paper states it uses the architecture from Kingma & Dhariwal (2018) and varies the number of flows per level (D=8, D=4), but it does not provide specific hyperparameters such as learning rates, batch sizes, optimizers, or training schedules in the main text. |