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..
COT-GAN: Generating Sequential Data via Causal Optimal Transport
Authors: Tianlin Xu, Li Kevin Wenliang, Michael Munn, Beatrice Acciaio
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show effectiveness and stability of COT-GAN when generating both lowand high-dimensional time series data. and We now validate COT-GAN empirically1. |
| Researcher Affiliation | Collaboration | Tianlin Xu London School of Economics EMAIL Li K. Wenliang University College London EMAIL Michael Munn Google, NY EMAIL Beatrice Acciaio London School of Economics ETH Zurich EMAIL |
| Pseudocode | Yes | Algorithm 1: training COT-GAN by SGD |
| Open Source Code | Yes | Code and data are available at github.com/tianlinxu312/cot-gan |
| Open Datasets | Yes | This dataset is from the UCI repository [18] and [18] D. Dua and C. Graff. UCI Machine Learning Repository. 2017. |
| Dataset Splits | No | The paper mentions using datasets like AR-1, noisy oscillations, EEG, Sprites, and human action sequences, but it does not explicitly provide specific training, validation, or test split percentages or sample counts in the main text. |
| Hardware Specification | No | The paper does not specify the GPU models, CPU models, or other detailed hardware specifications used for running experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or their version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | We pre-process the Sprites sequences to have a sequence length of T = 13, and the human action sequences to have length T = 16. Each frame has dimension 64 64 3. We employ the same architecture for the generator and discriminator to train both datasets. Both the generator and discriminator consist of a generic LSTM with 2-D convolutional layers. Details of the data pre-processing, GAN architectures, hyper-parameter settings, and training techniques are reported in Appendix B.2. |