In this article, we propose a new class of priors for Bayesian inference with multiple Gaussian graphical models. We introduce fully Bayesian treatments of two popular procedures, the group graphical lasso and the fused graphical lasso, and extend them to a continuous spike-and-slab framework to allow self-adaptive shrinkage and model selection simultaneously. We develop an EM algorithm that performs fast and dynamic explorations of posterior modes. Our approach selects sparse models efficiently with substantially smaller bias than would be induced by alternative regularization procedures. The performance of the proposed methods are demonstrated through simulation and two real data examples.

In this paper, we focus on the supervised learning problem with corrupted training data. We assume that the training dataset is generated from a mixture of a target distribution and other unknown distributions. We estimate the quality of each data by revealing the correlation between the generated distribution and the target distribution. To this end, we present a novel framework referred to here as ChoiceNet that can robustly infer the target distribution in the presence of inconsistent data. We demonstrate that the proposed framework is applicable to both classification and regression tasks. ChoiceNet is evaluated in comprehensive experiments, where we show that it constantly outperforms existing baseline methods in the handling of noisy data. Particularly, ChoiceNet is successfully applied to autonomous driving tasks where it learns a safe driving policy from a dataset with mixed qualities. In the classification task, we apply the proposed method to the MNIST and CIFAR-10 datasets and it shows superior performances in terms of robustness to noisy labels.

Machine learning shows promise for improving our understanding of many-body problems. Tackling an unsolved problem, or classifying intricate phases, remains however a daunting task. Building on a recently introduced interpretable supervised learning scheme, we introduce a generic protocol to probe and identify nonlinear orientational spin order, and extract the analytical form of the tensorial order parameter up to rank 6. Moreover, we find that our approach yields reliable results already for a modest amount of training data and without knowledge of the exact phase boundary. Our approach may prove useful for identifying novel spin order and ruling out spurious spin liquid candidates.

Unsupervised Machine Learning Projects with R will help you build your knowledge and skills by guiding you in building machine learning projects with a practical approach and using the latest technologies provided by the R language such as Rmarkdown, R-shiny, and more. The areas this course addresses include effectively exploring and preparing data in R and RStudio and training, evaluating, and improving a model's performance (if needed). You will feel comfortable and confident after learning unsupervised and supervised Machine Learning algorithms. In the first of the four sections comprising this course, we start by introducing you to concepts in Machine Learning, before then moving on to discuss projects in unsupervised Machine Learning. Next, we focus on two machine learning paradigms--K-Means Clustering and Principal Component Analysis--to grasp how they work and apply them to business Customer Segmentation (Market Segmentation Analysis).

Modern advancements in Artificial Intelligence (AI) are set to change our world for the better. These developments have largely been made possible due to technologies such as cloud sharing, data analytics, blockchain, and improved computing power. These technologies have significantly improved machine learning, the main cause driver behind AI advancements. Machine learning is probably the most important component of developing Artificial Intelligence. The process of machine learning involves running repeated simulations on a computer, recording the results, and then running new tests based on the previous outcomes.

You've probably heard about machine learning (ML). But you're not exactly sure how to use it in the context of GIS. Simply, machine learning makes sense out of noisy data finding patterns that you'd never think existed. In other words, it's software that writes software. Instead of applying a pre-built function, ML gains experience through repeated seen conditions and builds a model to apply in new situations.

Spectral dimensionality reduction methods enable linear separations of complex data with high-dimensional features in a reduced space. However, these methods do not always give the desired results due to irregularities or uncertainties of the data. Thus, we consider aggressively modifying the scales of the features to obtain the desired classification. Using prior knowledge on the labels of partial samples to specify the Fiedler vector, we formulate an eigenvalue problem of a linear matrix pencil whose eigenvector has the feature scaling factors. The resulting factors can modify the features of entire samples to form clusters in the reduced space, according to the known labels. In this study, we propose new dimensionality reduction methods supervised using the feature scaling associated with the spectral clustering. Numerical experiments show that the proposed methods outperform well-established supervised methods for toy problems with more samples than features, and are more robust regarding clustering than existing methods. Also, the proposed methods outperform existing methods regarding classification for real-world problems with more features than samples of gene expression profiles of cancer diseases. Furthermore, the feature scaling tends to improve the clustering and classification accuracies of existing unsupervised methods, as the proportion of training data increases.

Emotion recognition has become a popular topic of interest, especially in the field of human computer interaction. Previous works involve unimodal analysis of emotion, while recent efforts focus on multi-modal emotion recognition from vision and speech. In this paper, we propose a new method of learning about the hidden representations between just speech and text data using convolutional attention networks. Compared to the shallow model which employs simple concatenation of feature vectors, the proposed attention model performs much better in classifying emotion from speech and text data contained in the CMU-MOSEI dataset.

We propose a method to combine the interpretability and expressive power of firstorder logic with the effectiveness of neural network learning. In particular, we introduce a lifted framework in which first-order rules are used to describe the structure of a given problem setting. These rules are then used as a template for constructing a number of neural networks, one for each training and testing example. As the different networks corresponding to different examples share their weights, these weights can be efficiently learned using stochastic gradient descent. Our framework provides a flexible way for implementing and combining a wide variety of modelling constructs. In particular, the use of first-order logic allows for a declarative specification of latent relational structures, which can then be efficiently discovered in a given data set using neural network learning. Experiments on 78 relational learning benchmarks clearly demonstrate the effectiveness of the framework.

The development of microarray technology has made it possible to assemble biomedical datasets that measure the expression profile of thousands of genes simultaneously. However, such high-dimensional datasets make computation costly and can complicate the interpretation of a predictive model. To address this, feature selection methods are used to extract biological information from a large amount of data in order to filter the expression dataset down to the smallest possible subset of accurate predictor genes. Feature selection has three main advantages: it decreases computational costs, mitigates the possibility of overfitting due to high inter-variable correlations, and allows for an easier clinical interpretation of the model. In this paper we compare three methods of feature selection: iterative Bayesian Model Averaging (BMA), Random Survival Forest (RSF) and Cox Proportional Hazard (CPH) and five methods of survival analysis: Analysis RandomSurvival Forest (RSF), Cox Proportional Hazard (CPH), Alan Additive Filter (AAF), DeepSurv (neural network), andCbrSurv (case-based reasoning), which we introduce in this paper. Features selected by these methods are compared with a hand selected set of features. All the data we used came from the Metabric breast cancer dataset. Our results indicate that feature selection improves the performance of survival analysis methods. Overall, the best survival analysis performance was obtained by combining RSF for feature selection and CbrSurv, closely followed by DeepSurv, for survival prediction