Variational Temporal Abstraction
Authors: Taesup Kim, Sungjin Ahn, Yoshua Bengio
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In experiments, we demonstrate that our proposed method can model 2D and 3D visual sequence datasets with interpretable temporal structure discovery and that its application to jumpy imagination enables more efficient agent-learning in a 3D navigation task. |
| Researcher Affiliation | Collaboration | Taesup Kim1,3, , Sungjin Ahn2 , Yoshua Bengio1 1Mila, Université de Montréal, 2Rutgers University, 3Kakao Brain |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code of the implementation of our model is available at https://github.com/taesupkim/vta. |
| Open Datasets | No | The paper uses a self-generated 'bouncing balls' dataset and a self-collected dataset from a '3D maze environment' without providing public access information or citations to pre-existing public datasets. 'We generated a synthetic 2D visual sequence dataset called bouncing balls.' and 'Another sequence dataset is generated from the 3D maze environment by an agent that navigates the maze.' |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning into train/validation/test sets. |
| Hardware Specification | No | The paper mentions 'Kakao Brain cloud team for providing computing resources used in this work' but does not provide specific hardware details such as GPU/CPU models, processor types, or memory amounts. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers. |
| Experiment Setup | Yes | During training, the length of observation sequence data X is set to T = 20 and the context length is Tctx = 5. Hyper-parameters related to sequence decomposition are set as Nmax = 5 and lmax = 10. For HRSSM, we used the same training setting as bouncing balls but different Nmax = 5 and lmax = 8 for the sequence decomposition. ...controlled by annealing the temperature τ of Gumbel-softmax towards small values from 1.0 to 0.1. |