Implicit Generative Copulas
Authors: Tim Janke, Mohamed Ghanmi, Florian Steinke
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Section 4 shows experimental results for synthetic and real data from finance, physics, and image generation. |
| Researcher Affiliation | Academia | Tim Janke, Mohamed Ghanmi, Florian Steinke Energy Information Networks and Systems TU Darmstadt, Germany {tim.janke},{florian.steinke}@tu-darmstadt.de |
| Pseudocode | No | The paper describes the model and training procedure in text and mathematical equations, but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available from https://github.com/Tim CJanke/igc. |
| Open Datasets | Yes | We consider a data set of size N = 5844 that contains 15 years of daily exchange rates... The data was obtained from the R package qrm_data. MAGIC (Major Atmospheric Gamma-ray Imaging Cherenkov) Telescopes data set available from the UCI repository (https://archive.ics.uci.edu/ml/ datasets/MAGIC+Gamma+Telescope). Fashion MNIST [44] data set. |
| Dataset Splits | Yes | To ensure a test data set of appropriate size, we use a 5-fold cross-validation scheme where 20% of the data is used for training and 80% for testing. |
| Hardware Specification | Yes | All experiments besides the training of the autoencoder models were carried out on a desktop PC with a Intel Core i7-7700 3.60Ghz CPU and 8GB RAM. For the training of the autoencoders we used Google Colab [14]. |
| Software Dependencies | No | The paper mentions using 'tensorflow with the Keras API' and 'pyvinecopulib' and 'the R package kdecopula', but it does not specify any version numbers for these software components. |
| Experiment Setup | Yes | For all experiments except the image generation task, we use a fully connected neural network with two layers, 100 units per layer, Re LU activation functions, and train for 500 epochs. For the image generation experiment we use a three layer, fully connected neural network with 200 neurons in each layer, and train for 100 epochs. In all cases we train with a batch size of Nbatch = 100 and generate M = 200 samples from the model per batch. The number of noise distributions is set as K = 3D and T = 106. We use the Adam optimizer [20] with default parameters. |