Researchers often summarize their work in the form of posters. Posters provide a coherent and efficient way to convey core ideas from scientific papers. Generating a good scientific poster, however, is a complex and time consuming cognitive task, since such posters need to be readable, informative, and visually aesthetic. In this paper, for the first time, we study the challenging problem of learning to generate posters from scientific papers. To this end, a data-driven framework, that utilizes graphical models, is proposed. Specifically, given content to display, the key elements of a good poster, including panel layout and attributes of each panel, are learned and inferred from data. Then, given inferred layout and attributes, composition of graphical elements within each panel is synthesized. To learn and validate our model, we collect and make public a Poster-Paper dataset, which consists of scientific papers and corresponding posters with exhaustively labelled panels and attributes. Qualitative and quantitative results indicate the effectiveness of our approach.

If you're relatively new to Machine Learning and it's applications, you'll more than likely have come across some pretty technical terms that are often difficult for the novice mathematician/scientist to get their head around. Following on from a previous blog, (10 Common NLP Terms Explained for the Text Analysis Novice), we decided to put together a list of 10 Machine Learning terms which have been broken down in simple English, making them easier to understand. So, if you're struggling to understand the difference between Supervised and Un-supervised Learning you'll enjoy this post. A subfield of computer science and artificial intelligence (AI) that focuses on the design of systems that can learn from and make decisions and predictions based on data. Machine learning enables computers to act and make data-driven decisions rather than being explicitly programmed to carry out a certain task.

I wrote a series of blog posts on Bayesian modeling with R and Stan. Stan is a growing platform for MC(MC) computing implemented with C . Compared to WinBUGS or OpenBUGS, it is very fast and programmable intuitively. This series of the posts show how to install Stan on R, how to run it, and how to apply it to actual datasets. I hope you'll find it to practice Bayesian modeling easier than ever.

Many retailers today employ inventory management systems based on Re-Order Point Policies, most of which rely on the assumption that all decreases in product inventory levels result from product sales. Unfortunately, it usually happens that small but random quantities of the product get lost, stolen or broken without record as time passes, e.g., as a consequence of shoplifting. This is usual for retailers handling large varieties of inexpensive products, e.g., grocery stores. In turn, over time these discrepancies lead to stock freezing problems (see Ref. [1]), i.e., situations where the system believes the stock is above the reorder point but the actual stock is at zero, and so no replenishments or sales occur. Motivated by these issues, we model the interaction between sales, losses, replenishments and inventory levels as a Dynamic Bayesian Network (DBN), where the inventory levels are unobserved (i.e., hidden) variables we wish to estimate. We present an Expectation-Maximization (EM) algorithm to estimate the parameters of the sale and loss distributions, which relies on solving a one-dimensional dynamic program for the E-step and on solving two separate one-dimensional nonlinear programs for the M-step.

This is part 3/3 of a series on deep belief networks. Part 1 focused on the building blocks of deep neural nets – logistic regression and gradient descent. Part 2 focused on how to use logistic regression as a building block to create neural networks, and how to train them. Part 3 will focus on answering the question: "What is a deep belief network?" and the algorithms we use to do training and prediction. In its simplest form, a deep belief network looks exactly like the artificial neural networks we learned about in part 2! As long as there is at least 1 hidden layer, the model is considered to be "deep".

Reading the academic literature Text Analytics seems difficult. However, applying it in practice has shown us that Text Classification is much easier than it looks. Most of the Classifiers consist of only a few lines of code.In this three-part blog series we will examine the three well-known Classifiers; the Naive Bayes, Maximum Entropy and Support Vector Machines. From the introductionary blog we know that the Naive Bayes Classifier is based on the bag-of-words model. With the bag-of-words model we check which word of the text-document appears in a positive-words-list or a negative-words-list.

So you know the Bayes rule. How does it relate to machine learning? It can be quite difficult to grasp how the puzzle pieces fit together – we know it took us a while. This article is an introduction we wish we had back then. While we have some grasp on the matter, we're not experts, so the following might contain inaccuracies or even outright errors.

Models with latent variables have been proposed and investigated for explaining, understanding, or classifying observed data. If a model is a generative model, observed data are modeled to be as if they were generated by latent variables through parameterized probability distributions. Popular criteria for learning generative models include likelihood or posterior probability, which both evaluate the probability of the given observed data or parameters. Another kind of criteria is mutual information. Mutual information has been used to learn nonlinear generative models [14] in which relationships between observed and latent variables are directly evaluated. It has also been used to learn linear encoding (recognition) models [2, 12]. The relationships between observed and latent variables have greater importance in more complex generative models, e.g., deep learning models [6, 9]. In the pre-training of deep belief networks (DBNs), one of the models or techniques of deep learning, posterior samples of latent variables in the lower layer are used as samples of observed variables in the next, higher layer. For successive layer learning to be possible, latent variables should possess properties that enable such learning.

We study the robustness of active learning (AL) algorithms against prior misspecification: whether an algorithm achieves similar performance using a perturbed prior as compared to using the true prior. In both the average and worst cases of the maximum coverage setting, we prove that all $\alpha$-approximate algorithms are robust (i.e., near $\alpha$-approximate) if the utility is Lipschitz continuous in the prior. We further show that robustness may not be achieved if the utility is non-Lipschitz. This suggests we should use a Lipschitz utility for AL if robustness is required. For the minimum cost setting, we can also obtain a robustness result for approximate AL algorithms. Our results imply that many commonly used AL algorithms are robust against perturbed priors. We then propose the use of a mixture prior to alleviate the problem of prior misspecification. We analyze the robustness of the uniform mixture prior and show experimentally that it performs reasonably well in practice.

You know that you want to build a predictive model. You've framed your problem in terms of classification or regression. You've prepared some training data (which took an age). You've heard or experienced first hand that Random Forests, Elastic Net Regression or Deep Belief Networks are "the business" and so you're going to use one of these (you've probably already verified that these algorithms are appropriate to your problem based on their general capabilities: whether it be their ability to deal with real valued data, "big" streaming data, multiple classes and so on). However, no two algorithms are the same (if they were we'd simply have fewer to choose from). As such there are a host of questions that you may not have even thought to ask which could make or break your choice.