Scalable Normalizing Flows for Permutation Invariant Densities
Authors: Marin Biloš, Stephan Günnemann
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In the experiments we aim to show how having an exact trace model does not limit the expressiveness on the contrary we get the benefits of the faster and more stable training. First, we show that our versions of the models are equivalent to their original implementation, then demonstrate the modeling capacity for the point processes, and finally, show how we can scale to bigger datasets. |
| Researcher Affiliation | Academia | Marin Biloš 1 Stephan Günnemann 1 1Technical University of Munich, Germany. |
| Pseudocode | No | No pseudocode or algorithm blocks found in the paper. |
| Open Source Code | No | The detailed explanation of the data processing, hyperparameter tuning, and additional results can be found in the Supplementary Material.1 https://www.daml.in.tum.de/scalable-nf |
| Open Datasets | Yes | Check-ins NY (Cho et al., 2011) is a collection of locations of social network users. [...] Crimes2 dataset contains daily records of locations and types of crimes that occurred in Portland. 2https://nij.ojp.gov/funding/real-time-crime-forecasting-challenge |
| Dataset Splits | Yes | Datasets are split into training, validation and test sets (60%-20%-20%). |
| Hardware Specification | Yes | We train all of our models on a single GPU (12GB). |
| Software Dependencies | No | No specific software dependencies with version numbers are explicitly listed in the paper. |
| Experiment Setup | Yes | We train with early stopping, use mini-batches of size 64 and Adam optimizer with the learning rate of 10 3 (Kingma & Ba, 2015). |