Measuring Dependence with Matrix-based Entropy Functional
Authors: Shujian Yu, Francesco Alesiani, Xi Yu, Robert Jenssen, Jose Principe10781-10789
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also show the impact of our measures in four different machine learning problems, namely the gene regulatory network inference, the robust machine learning under covariate shift and non-Gaussian noises, the subspace outlier detection, and the understanding of the learning dynamics of convolutional neural networks, to demonstrate their utilities, advantages, as well as implications to those problems. |
| Researcher Affiliation | Collaboration | Shujian Yu1, Francesco Alesiani1, Xi Yu2, Robert Jenssen3, Jose Principe2 1 NEC Laboratories Europe 2 University of Florida 3 UiT The Arctic University of Norway |
| Pseudocode | No | The paper describes its proposed measures and their properties through mathematical formulations and text, but it does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code of our measures and supplementary material of this work are available at: https://bit.ly/AAAI-dependence. |
| Open Datasets | Yes | We resorted to the DREAM4 challenge (Marbach et al. 2012) data set for reconstructing GRN. [...] the source data is the Fashion-MNIST dataset (Xiao, Rasul, and Vollgraf 2017). [...] We select the widely used bike sharing data set (Fanaee-T and Gama 2014) in UCI repository. [...] We test on 5 publicly available data sets from the Outlier Detection Data Sets (ODDS) library (Rayana 2016). |
| Dataset Splits | Yes | We use the first three seasons samples as source data and the forth season samples as target data. |
| Hardware Specification | No | The paper describes the software setup, models, and training parameters, but it does not specify any hardware details such as CPU/GPU models, memory, or specific computing environments used for the experiments. |
| Software Dependencies | No | The paper mentions optimizers like Adam and SGD, but does not provide specific version numbers for any software libraries, frameworks, or programming languages used in the experiments. |
| Experiment Setup | Yes | The neural network architecture is set as: there are 2 convolutional layers (with, respectively, 16 and 32 filters of size 5 5) and 1 fully connected layers. We add batch normalization and max-pooling layer after each convolutional layer. We choose Re LU activation, batch size 128 and the Adam optimizer (Kingma and Ba 2014). [...] The model of choice is a multi-layered perceptron (MLP) with three hidden layer of size 100, 100 and 10 respectively. We use batch-size of 32 and the Adam optimizer. |