Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Closing the Dequantization Gap: PixelCNN as a Single-Layer Flow
Authors: Didrik Nielsen, Ole Winther
NeurIPS 2020 | Venue PDF | LLM Run Details | Input Tokens: 16,817 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,522 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we study multilayer flows composed of Pixel CNNs and non-autoregressive coupling layers and demonstrate state-of-the-art results on CIFAR-10 for flow models trained with dequantization. 6 Experiments |
| Researcher Affiliation | Academia | Didrik Nielsen Technical University of Denmark EMAIL Ole Winther Technical University of Denmark EMAIL |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | Yes | The code used for experiments is publicly available at https://github.com/ didriknielsen/pixelcnn_flow. |
| Open Datasets | Yes | We trained Pixel CNN (van den Oord et al., 2016c) and Pixel CNN++ (Salimans et al., 2017) as flow models on CIFAR-10 |
| Dataset Splits | No | The paper mentions using CIFAR-10, which has standard splits, but does not explicitly provide the train/validation/test split percentages or sample counts within the text. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | The paper describes the architectural composition of models and the dequantization method used, but does not provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs, optimizer settings) or a detailed table/paragraph of training configurations. |