Marginal Tail-Adaptive Normalizing Flows
Authors: Mike Laszkiewicz, Johannes Lederer, Asja Fischer
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | An empirical analysis shows that the proposed method improves on the accuracy especially on the tails of the distribution and is able to generate heavytailed data. We demonstrate its application on a weather and climate example, in which capturing the tail behavior is essential. |
| Researcher Affiliation | Academia | 1Faculty of Mathematics, Ruhr University, Bochum, Germany 2Center of Computer Science, Bochum, Germany. |
| Pseudocode | Yes | Algorithm 1 Marginal Tail Estimation |
| Open Source Code | Yes | We provide a Py Torch implementation and the code for all experiments, which can be accessed through our public git repository8. |
| Open Datasets | Yes | We apply m TAF to generate new data following the distribution of the EUMETSAT Numerical Weather Prediction Satellite Application Facility (NWP-SAF) dataset (Eresmaa & Mc-Nally, 2014). |
| Dataset Splits | Yes | To construct the training, test, and validation sets 15.000, 75 000, and 10 000 samples from this distribution are sampled, respectively. [...] Training, validation, and test sets consists of 50 000, 10 000, and 75 000 samples, respectively. |
| Hardware Specification | No | The paper does not specify any particular hardware (e.g., CPU, GPU models, memory) used for the experiments. |
| Software Dependencies | No | The paper mentions 'Py Torch implementation' but does not provide specific version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | We optimized the network using Adam with 5 000 or 20 000 train steps in the case D = 8 and D = 50, respectively, with a learning rate of 1e 5 and a weight decay of 1e 6. To fit the Gaussian copula baseline, we use the default settings of the copulas (Patki et al., 2016) library. [...] In the NSF layers, we used conditioner Res Nets with 2 hidden layers with 30 or 200 hidden neurons in the case D = 8 and D = 50, respectively and Re LU activations. Further, we used NSF layers with 3 bins and set the tail-bound to 2. [...] The conditioner networks in the NSF-layers have 2 hidden layers with 100 hidden neurons in each layer, we set the tail-bounds to 2.5, and each spline uses 3 bins. We apply Batch-Norm after each NSF-layer. We optimize for 20 000 steps using the Adam optimizer with a learning rate of 1e-4 and a learning rate of 0.01 for the tail indices and scheduled the rates using cosine annealing. |