Learning Representations of Sets through Optimized Permutations
Authors: Yan Zhang, Jonathon Hare, Adam Prügel-Bennett
ICLR 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In four different experiments, we show improvements over existing methods (section 4). on which we achieve state-of-the-art results: number sorting, image mosaics, classification from image mosaics, and visual question answering. |
| Researcher Affiliation | Academia | Yan Zhang & Adam Pr ugel-Bennett & Jonathon Hare Department of Electronics and Computer Science University of Southampton {yz5n12,apb,jsh2}@ecs.soton.ac.uk |
| Pseudocode | Yes | A PSEUDOCODE OF ALGORITHM |
| Open Source Code | Yes | Precise experimental details can be found in Appendix F and our implementation for all experiments is available at https: //github.com/Cyanogenoid/perm-optim for full reproducibility. |
| Open Datasets | Yes | We take these images from either MNIST, CIFAR10, or a version of Image Net with images resized down to 64 64 pixels. We use the VQA v2 dataset (Antol et al., 2015; Goyal et al., 2017). |
| Dataset Splits | Yes | We use the VQA v2 dataset (Antol et al., 2015; Goyal et al., 2017), which in total contains around 1 million questions about 200,000 images from MS-COCO with 6.5 million human-provided answers available for training. Our results on the validation set of VQA v2 are shown in Table 3. |
| Hardware Specification | No | The paper mentions "GPU memory requirements" but does not specify any particular GPU model, CPU, or other hardware components used for running experiments. |
| Software Dependencies | No | All of our experiments can be reproduced using our implementation at https:// github.com/Cyanogenoid/perm-optim in Py Torch (Paszke et al., 2017) |
| Experiment Setup | Yes | All of our experiments can be reproduced using our implementation at https:// github.com/Cyanogenoid/perm-optim in Py Torch (Paszke et al., 2017) through the experiments/all.sh script. For the former three experiments, we use the following hyperparameters throughout: Optimiser: Adam (Kingma & Ba, 2015) (default settings in Py Torch: β1 = 0.9, β2 = 0.999, ϵ = 10 8) Initial step size η in inner gradient descent: 1.0 Inner gradient descent steps T: 6 Adam learning rate: 0.1 Batch size: 512 Number of sets to sort in training set: 218 Adam learning rate: 10 3 Inner gradient descent steps T: 4 Batch size: 32 Training epochs: 20 (MNIST, CIFAR10) or 1 (Image Net) F size of hidden dimension: 64 (MNIST, CIFAR10) or 128 (Image Net). |