We present a novel method for hierarchical topic detection where topics are obtained by clustering documents in multiple ways. Specifically, we model document collections using a class of graphical models called hierarchical latent tree models (HLTMs). The variables at the bottom level of an HLTM are observed binary variables that represent the presence/absence of words in a document. The variables at other levels are binary latent variables, with those at the lowest latent level representing word co-occurrence patterns and those at higher levels representing co-occurrence of patterns at the level below. Each latent variable gives a soft partition of the documents, and document clusters in the partitions are interpreted as topics. Latent variables at high levels of the hierarchy capture long-range word co-occurrence patterns and hence give thematically more general topics, while those at low levels of the hierarchy capture short-range word co-occurrence patterns and give thematically more specific topics. Unlike LDA-based topic models, HLTMs do not refer to a document generation process and use word variables instead of token variables. They use a tree structure to model the relationships between topics and words, which is conducive to the discovery of meaningful topics and topic hierarchies.

This blog post was originally published as part of an ongoing series, "Popular Algorithms Explained in Simple English" on the AYLIEN Text Analysis Blog. Commonly used in Machine Learning, Naive Bayes is a collection of classification algorithms based on Bayes Theorem. It is not a single algorithm but a family of algorithms that all share a common principle, that every feature being classified is independent of the value of any other feature. So for example, a fruit may be considered to be an apple if it is red, round, and about 3" in diameter. A Naive Bayes classifier considers each of these "features" (red, round, 3" in diameter) to contribute independently to the probability that the fruit is an apple, regardless of any correlations between features.

Bayesian Reasoning and Machine Learning by David Barber has a chapter on Approximate Sampling Christophe Andrieu et al. have written an introductory tutorial (pdf) on MCMC methods that covers most of the MCMC algorithms Dr. Daphne Koller offers an online course on Coursera, Probabilistic Graphical Models, which also covers the Gibbs Sampler and the Metropolis-Hastings Algorithm Dr. A. Taylan Cemgil has prepared very useful lecture notes (pdf) for his Monte Carlo methods course

According to the American Cancer Society, cancer kills more than 8 million people each year. Early detection can boost survival rates. Researchers and clinicians are feverishly exploring avenues to provide early and accurate diagnoses, as well as more targeted treatments. Blood screenings are used to detect many types of cancers, including liver, ovarian, colon and lung cancers. Current blood screening methods typically rely on affixing biochemical labels to cells or biomolecules.

Presbyterian reverend Thomas Bayes had no reason to suspect he'd make any lasting contribution to humankind. Born in England at the beginning of the 18th century, Bayes was a quiet and questioning man. He published only two works in his lifetime. In 1731, he wrote a defense of God's--and the British monarchy's--"divine benevolence," and in 1736, an anonymous defense of the logic of Isaac Newton's calculus. Yet an argument he wrote before his death in 1761 would shape the course of history.

We introduce a class of network models that insert edges by connecting the starting and terminal vertices of a random walk on the network graph. Within the taxonomy of statistical network models, this class is distinguished by permitting the location of a new edge to explicitly depend on the structure of the graph, but being nonetheless statistically and computationally tractable. In the limit of infinite walk length, the model converges to an extension of the preferential attachment model---in this sense, it can be motivated alternatively by asking what preferential attachment is an approximation to. Theoretical properties, including the limiting degree sequence, are studied analytically. If the entire history of the graph is observed, parameters can be estimated by maximum likelihood. If only the final graph is available, its history can be imputed using MCMC. We develop a class of sequential Monte Carlo algorithms that are more generally applicable to sequential random graph models, and may be of interest in their own right. The model parameters can be recovered from a single graph generated by the model. Applications to data clarify the role of the random walk length as a length scale of interactions within the graph.

Within Machine Learning many tasks are - or can be reformulated as - classification tasks. In classification tasks we are trying to produce a model which can give the correlation between the input data and the class each input belongs to. This model is formed with the feature-values of the input-data. For example, the dataset contains datapoints belonging to the classes Apples, Pears and Oranges and based on the features of the datapoints (weight, color, size etc) we are trying to predict the class. We need some amount of training data to train the Classifier, i.e. form a correct model of the data.

In case you've been hanging out with the technology groundhog in its cave: we're in the midst of an AI spring. The past two years have seen a resurgence in excitement around our ability to model human-like intelligence in computer algorithms. This excitement has a number of catalysts, not least of which is the enabled application of deep neural networks to a multitude of fields by the advancement of Moore's law. For the average person, the AI spring is a period of unbridled excitement: your iPhone will transcribe your voicemails so you don't have to lift the phone to your ear, Facebook will translate the posts of the friends you made during that one summer in college in Puerto Rico, and your Alexa can tell you interesting trivia about Star Wars. But the life of a startup CEO is not so simple.

Data Science for IoT vs Classic Data Science: 10 Differences Enterprise AI insights from the AI Europe event in London Is it time to consider data in motion in your big data projects?

The challenging task of learning structures of probabilistic graphical models is an important problem within modern AI research. Recent years have witnessed several major algorithmic advances in structure learning for Bayesian networks---arguably the most central class of graphical models---especially in what is known as the score-based setting. A successful generic approach to optimal Bayesian network structure learning (BNSL), based on integer programming (IP), is implemented in the GOBNILP system. Despite the recent algorithmic advances, current understanding of foundational aspects underlying the IP based approach to BNSL is still somewhat lacking. Understanding fundamental aspects of cutting planes and the related separation problem( is important not only from a purely theoretical perspective, but also since it holds out the promise of further improving the efficiency of state-of-the-art approaches to solving BNSL exactly. In this paper, we make several theoretical contributions towards these goals: (i) we study the computational complexity of the separation problem, proving that the problem is NP-hard; (ii) we formalise and analyse the relationship between three key polytopes underlying the IP-based approach to BNSL; (iii) we study the facets of the three polytopes both from the theoretical and practical perspective, providing, via exhaustive computation, a complete enumeration of facets for low-dimensional family-variable polytopes; and, furthermore, (iv) we establish a tight connection of the BNSL problem to the acyclic subgraph problem.