BRUNO: A Deep Recurrent Model for Exchangeable Data
Authors: Iryna Korshunova, Jonas Degrave, Ferenc Huszar, Yarin Gal, Arthur Gretton, Joni Dambre
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The advantages of our architecture are demonstrated on learning tasks that require generalisation from short observed sequences while modelling sequence variability, such as conditional image generation, few-shot learning, and anomaly detection. |
| Researcher Affiliation | Collaboration | Iryna Korshunova Ghent University iryna.korshunova@ugent.be Jonas Degrave Ghent University jonas.degrave@ugent.be Ferenc Huszár Twitter fhuszar@twitter.com Yarin Gal University of Oxford yarin@cs.ox.ac.uk Arthur Gretton Gatsby Unit, UCL arthur.gretton@gmail.com Joni Dambre Ghent University joni.dambre@ugent.be |
| Pseudocode | No | The paper does not contain any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at github.com/Ira Korshunova/bruno. |
| Open Datasets | Yes | The model was trained on Omniglot [10] same-class image sequences of length 20... More generated samples from convolutional and non-convolutional architectures trained on MNIST [11], Fashion-MNIST [22] and CIFAR-10 [9] are given in the appendix. |
| Dataset Splits | Yes | The model was trained on Omniglot [10] same-class image sequences of length 20 and we used the train-test split and preprocessing as defined by Vinyals et al. [21]. Namely, we resized the images to 28 28 pixels and augmented the dataset with rotations by multiples of 90 degrees yielding 4,800 and 1,692 classes for training and testing respectively. |
| Hardware Specification | Yes | Training was run on a single Titan X GPU for 24 hours. |
| Software Dependencies | No | The paper mentions software components like "Adam [7] optimizer" (Appendix C) but does not provide specific version numbers for these, nor for any programming languages or deep learning frameworks used. |
| Experiment Setup | Yes | All models were trained using Adam [7] optimizer with a learning rate of 10^ 4 and a batch size of 16. |