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..
Parallel Multiscale Autoregressive Density Estimation
Authors: Scott Reed, Aäron Oord, Nal Kalchbrenner, Sergio Gómez Colmenarejo, Ziyu Wang, Yutian Chen, Dan Belov, Nando Freitas
ICML 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the model on class-conditional image generation, text-to-image synthesis, and action-conditional video generation, showing that our model achieves the best results among non-pixel-autoregressive density models that allow efficient sampling. |
| Researcher Affiliation | Industry | 1Deep Mind. Correspondence to: Scott Reed <EMAIL>. |
| Pseudocode | No | The paper describes the model architecture and process in text and diagrams (Figure 2, Figure 3) but does not include explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about open-source code release or a link to a code repository for the described methodology. |
| Open Datasets | Yes | We evaluate our model on Image Net (Deng et al., 2009), Caltech-UCSD Birds (CUB) (Wah et al., 2011), the MPII Human Pose dataset (MPII) (Andriluka et al., 2014), the Microsoft Common Objects in Context dataset (MS-COCO) (Lin et al., 2014), and the Google Robot Pushing dataset (Finn et al., 2016). |
| Dataset Splits | Yes | There are 50, 000 training sequences and a validation set with the same objects but di erent arm trajectories. One test set involves a subset of the objects seen during training and another involving novel objects, both captured on an arm and camera viewpoint not seen during training. |
| Hardware Specification | Yes | Table 4. Sampling speed of several models in seconds per frame on an Nvidia Quadro M4000 GPU. |
| Software Dependencies | No | The paper mentions using 'Tensor Flow' for image resizing ('tf.image.resize_images') but does not specify its version or any other software dependencies with version numbers. |
| Experiment Setup | Yes | All models for Image Net, CUB, MPII and MS-COCO were trained using RMSprop with hyperparameter = 1e 8, with batch size 128 for 200K steps. The learning rate was set initially to 1e 4 and decayed to 1e 5. |