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.
PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications
Authors: Tim Salimans, Andrej Karpathy, Xi Chen, Diederik P. Kingma
ICLR 2017 | Venue PDF | LLM Run Details | Input Tokens: 13,208 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,903 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 present state-of-the-art log likelihood results on CIFAR-10 to demonstrate the usefulness of these modifications. |
| Researcher Affiliation | Industry | Tim Salimans, Andrej Karpathy, Xi Chen, Diederik P. Kingma EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Here we discuss our implementation of Pixel CNNs which we make available at https://github.com/openai/pixel-cnn. |
| Open Datasets | Yes | We apply our model to modeling natural images in the CIFAR-10 data set. |
| Dataset Splits | No | The paper mentions training on CIFAR-10 and reporting test log-likelihood, but does not explicitly state the specific train/validation/test dataset splits used for reproduction. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions a 'tensorflow implementation' but does not provide specific version numbers for TensorFlow or any other software dependencies. |
| Experiment Setup | Yes | For the encoding part of the Pixel CNN, the model uses 3 Resnet blocks consisting of 5 residual layers, with 2 2 downsampling in between. The same architecture is used for the decoding part of the model, but with upsampling instead of downsampling in between blocks. All residual layers use 192 feature maps and a dropout rate of 0.5. |