Tails of Lipschitz Triangular Flows
Authors: Priyank Jaini, Ivan Kobyzev, Yaoliang Yu, Marcus Brubaker
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform several synthetic and real-world experiments to compliment our theoretical findings. |
| Researcher Affiliation | Collaboration | 1Univesity of Waterloo, Waterloo, Canada 2Vector Institute, Toronto, Canada 3Borealis AI 4York University, Toronto, Canada. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. There is no specific repository link, explicit code release statement, or mention of code in supplementary materials. |
| Open Datasets | Yes | Lastly, we replicate density estimation experiments on benchmark datasets popularly used to measure performance of flows and autoregressive models. |
| Dataset Splits | Yes | We divided the dataset into training-validation-testing in the ratio 2:1:1. |
| Hardware Specification | Yes | We thank NVIDIA Corporation (the data science grant) for donating two Titan V GPUs that enabled in part the computation in this work. |
| Software Dependencies | No | The paper mentions using "Adam (Kingma & Ba, 2014)" but does not specify version numbers for any software dependencies (e.g., Python, PyTorch, TensorFlow, or Adam itself). |
| Experiment Setup | Yes | We trained the model using Adam (Kingma & Ba, 2014) for 40 epochs with a batch size of 128 and learning rate of 10-3. |