General Purpose MRF Learning with Neural Network Potentials
Authors: Hao Xiong, Nicholas Ruozzi
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate experimentally that our approach is capable of effectively modeling the data distributions of a variety of real data sets and that it can compete effectively with other common methods on multilabel classification and generative modeling tasks. 4 Experimental Results In this section we will illustrate the practical performance of our method. |
| Researcher Affiliation | Academia | Hao Xiong and Nicholas Ruozzi The University of Texas at Dallas {hao.xiong, nicholas.ruozzi}@utdallas.edu |
| Pseudocode | Yes | Algorithm 1: MLE with NN potentials |
| Open Source Code | Yes | All code and data used as part of these experiments will be made available on Git Hub.1 (Footnote 1: https://github.com/motionlife/nnmrf) |
| Open Datasets | Yes | on eight data sets from the UCI repository and compare the results with a variety of standard classifiers: Support Vector Machines (SVMs), Gaussian Naive Bayes (GNB), logistic regression (LR) , decision trees (DTs), random forrests (RFs), k-nearest neighbor (KNN), and multi-layer perceptrons (MLP). ... as well as the MNIST image classification data set [Le Cun et al., 1998] |
| Dataset Splits | No | Table 1 shows the average test accuracy of all methods over 30 randomly chosen 80/20, train/test splits. The paper specifies train/test splits but does not provide details for a validation split. |
| Hardware Specification | No | Our algorithm was implemented in Python using the Tensor Flow 2 framework to take advantage of fast GPU training and inference. As a result, the entire training process can be implemented quite efficiently on modern GPU accelerators. The paper mentions GPUs generally but does not specify exact models or configurations. |
| Software Dependencies | No | Our algorithm was implemented in Python using the Tensor Flow 2 framework to take advantage of fast GPU training and inference. For the competing methods, we used the implementations available in scikit-learn. The paper mentions software names but does not provide version numbers for them. |
| Experiment Setup | Yes | Both node and edge potentials are neural nets with 4 hidden layers and the hidden nodes for each layer are [ 3, 5, 5, 3 ] for each node potential, [ 5, 7, 7, 5 ] for each edge potential respectively, with all hidden layer s activation function set to be Se LUs (scaled exponential linear units) [Klambauer et al., 2017] and the output layer activation is Tan H. We used a belief pool size of T = 20, and we ran 100 iterations in each marginal inference procedure during learning with the ADAM optimizer. |