Unsupervised Learning of Equivariant Structure from Sequences
Authors: Takeru Miyato, Masanori Koyama, Kenji Fukumizu
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conducted several experiments to investigate the efficacy of our framework. In this section, we briefly explain the experimental settings. For more details, please see Appendix D. We tested our framework on Sequential MNIST, 3DShapes [5], and Small NORB [48]. |
| Researcher Affiliation | Collaboration | Takeru Miyato 1,2 Masanori Koyama 1 Kenji Fukumizu3,1 equal contribution 1Preferred Networks, Inc. 2University of Tübingen 3The Institute of Statistical Mathematics |
| Pseudocode | No | The paper includes a diagram (Figure 2) to illustrate the procedure but does not contain a formal pseudocode or algorithm block. |
| Open Source Code | Yes | The code is available at https://github.com/takerum/meta_sequential_prediction. |
| Open Datasets | Yes | We tested our framework on Sequential MNIST, 3DShapes [5], and Small NORB [48]. Sequential MNIST is created from MNIST dataset [47]. |
| Dataset Splits | No | The paper mentions that it uses 'training and test sets' (Section D) and an internal split (sc, sp) for each sequence (Section 4), but it does not provide specific percentages, absolute sample counts, or clear predefined split references for the overall train/validation/test dataset splits. |
| Hardware Specification | Yes | We used one NVIDIA A100-80GB GPU to train each model. |
| Software Dependencies | No | The paper mentions optimizers (Adam) and architectures (ResNet), but it does not specify any software libraries or frameworks with their version numbers (e.g., PyTorch version, Python version, specific package versions) required to reproduce the experiments. |
| Experiment Setup | Yes | We trained our models using Adam [37] for 500 epochs with a learning rate of 1e-4. The batch size was set to 128. We used Layer Normalization [2] in our ResNet architecture and used weight standardization [55] for convolution layers. |