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..
PixelSNAIL: An Improved Autoregressive Generative Model
Authors: XI Chen, Nikhil Mishra, Mostafa Rohaninejad, Pieter Abbeel
ICML 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this paper, we describe the resulting model and present state-of-the-art log-likelihood results on heavily benchmarked datasets: CIFAR-10 (2.85 bits per dim), 32 32 Image Net (3.80 bits per dim) and 64 64 Image Net (3.52 bits per dim). |
| Researcher Affiliation | Collaboration | 1covariant.ai 2UC Berkeley, EECS Dept.. |
| Pseudocode | No | Figure 4 shows diagrams of the Residual Block and Attention Block components, but these are flowcharts/schematics rather than structured text-based pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code will be made available, and can be found at: https://github.com/neocxi/ pixelsnail-public. |
| Open Datasets | Yes | CIFAR-10, 32 32 Image Net and 64 64 Image Net |
| Dataset Splits | No | The paper mentions using Polyak averaging over training parameters and specifies dataset properties and mixture components but does not explicitly describe the methodology for creating or using validation splits. |
| Hardware Specification | No | Due to computational limits, we can only train these models on 4 GPUs but are able to outperform the previous state-of-the-art model that was trained on 32 GPUs (van den Oord et al., 2016b). |
| Software Dependencies | No | The paper mentions techniques like 'Polyak averaging' and 'discretized mixture of logistics' and 'Weight Normalization' but does not specify software dependencies (e.g., libraries, frameworks) with version numbers. |
| Experiment Setup | Yes | For both datasets, we used residual blocks with 256 filters and 4 repeats, and attention blocks with key size 16 and value size 128. In the CIFAR-10 model only, we applied dropout of 0.5 after the first convolution in every residual block, to prevent overfitting. We used an exponential moving average weight of 0.9995 for CIFAR-10 and 0.9997 for Image Net. As the output distribution, we use the discretized mixture of logistics introduced by Salimans et al. (2017), with 10 mixture components for CIFAR-10 and 32 for Image Net. We used 12 blocks for both datasets, with 10 mixture components for CIFAR-10 and 32 for Image Net. |