Modelling heterogeneous distributions with an Uncountable Mixture of Asymmetric Laplacians
Authors: Axel Brando, Jose A. Rodriguez, Jordi Vitria, Alberto Rubio Muñoz
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6 Experimental ResultsAll experiments are implemented in Tensor Flow [33] and Keras [34], running in a workstation with Titan X (Pascal) GPU and Ge Force RTX 2080 GPU. |
| Researcher Affiliation | Collaboration | Axel Brando BBVA Data & Analytics Universitat de Barcelona Jose A. Rodríguez-Serrano BBVA Data & Analytics Jordi Vitrià Universitat de Barcelona Alberto Rubio BBVA Data & Analytics |
| Pseudocode | Yes | Algorithm 2: How to build UMAL model by using any deep learning architecture for regression |
| Open Source Code | Yes | The source code to reproduce the public results reported is published in https://github.com/BBVA/UMAL. |
| Open Datasets | Yes | By using the publicly available information from the the Inside Airbnb platform [17] we selected Barcelona (BCN) and Vancouver (YVC) as the cities to carry out the comparison of the models in a real situation. ... [17] Murray Cox. Inside airbnb: adding data to the debate. Inside Airbnb [Internet].[cited 16 May 2019]. Available: http://insideairbnb.com, 2019. |
| Dataset Splits | Yes | A total of 50% of the random uniform generated data were considered as test data, 40% for training and 10% for validation. |
| Hardware Specification | Yes | All experiments are implemented in Tensor Flow [33] and Keras [34], running in a workstation with Titan X (Pascal) GPU and Ge Force RTX 2080 GPU. |
| Software Dependencies | No | Software used (Tensor Flow, Keras) is mentioned, but specific version numbers are not provided. |
| Experiment Setup | Yes | Regarding parameters, we use a common learning rate of 10 3. In addition, to restrict the value of the scale parameter, b, to strictly positive values, the respective output have a softplus function [35] as activation. We will refer to the number of parameters to be estimated as P. On the other hand, the Monte Carlo sampling number, Nτ, for Independent QR, ALD and UMAL models will always be fixed to 100 at the training time. Furthermore, all public experiments are trained using an early stopping training policy with 200 epochs of patience for all compared methods. |