Emerging Convolutions for Generative Normalizing Flows
Authors: Emiel Hoogeboom, Rianne Van Den Berg, Max Welling
ICML 2019 | Conference PDF | Archive PDF | Plain Text | 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 <e.hoogeboom@uva.nl>. |
| 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. |