Learning and Interpreting Multi-Multi-Instance Learning Networks

We introduce an extension of the multi-instance learning problem where examples are organized as nested bags of instances (e.g., a document could be represented as a bag of sentences, which in turn are bags of words). This framework can be useful in various scenarios, such as text and image classification, but also supervised learning over graphs. As a further advantage, multi-multi instance learning enables a particular way of interpreting predictions and the decision function. Our approach is based on a special neural network layer, called bag-layer, whose units aggregate bags of inputs of arbitrary size. We prove theoretically that the associated class of functions contains all Boolean functions over sets of sets of instances and we provide empirical evidence that functions of this kind can be actually learned on semi-synthetic datasets. We finally present experiments on text classification, on citation graphs, and social graph data, which show that our model obtains competitive results with respect to accuracy when compared to other approaches such as convolutional networks on graphs, while at the same time it supports a general approach to interpret the learnt model, as well as explain individual predictions.Comment: JML

A Biologically Informed Hylomorphism

Although contemporary metaphysics has recently undergone a neo-Aristotelian revival wherein dispositions, or capacities are now commonplace in empirically grounded ontologies, being routinely utilised in theories of causality and modality, a central Aristotelian concept has yet to be given serious attention – the doctrine of hylomorphism. The reason for this is clear: while the Aristotelian ontological distinction between actuality and potentiality has proven to be a fruitful conceptual framework with which to model the operation of the natural world, the distinction between form and matter has yet to similarly earn its keep. In this chapter, I offer a first step toward showing that the hylomorphic framework is up to that task. To do so, I return to the birthplace of that doctrine - the biological realm. Utilising recent advances in developmental biology, I argue that the hylomorphic framework is an empirically adequate and conceptually rich explanatory schema with which to model the nature of organism

Looking Deeper into Deep Learning Model: Attribution-based Explanations of TextCNN

Layer-wise Relevance Propagation (LRP) and saliency maps have been recently used to explain the predictions of Deep Learning models, specifically in the domain of text classification. Given different attribution-based explanations to highlight relevant words for a predicted class label, experiments based on word deleting perturbation is a common evaluation method. This word removal approach, however, disregards any linguistic dependencies that may exist between words or phrases in a sentence, which could semantically guide a classifier to a particular prediction. In this paper, we present a feature-based evaluation framework for comparing the two attribution methods on customer reviews (public data sets) and Customer Due Diligence (CDD) extracted reports (corporate data set). Instead of removing words based on the relevance score, we investigate perturbations based on embedded features removal from intermediate layers of Convolutional Neural Networks. Our experimental study is carried out on embedded-word, embedded-document, and embedded-ngrams explanations. Using the proposed framework, we provide a visualization tool to assist analysts in reasoning toward the model's final prediction.Comment: NIPS 2018 Workshop on Challenges and Opportunities for AI in Financial Services: the Impact of Fairness, Explainability, Accuracy, and Privacy, Montr\'eal, Canad

From supernovae to neutron stars

The gravitational collapse, bounce, the explosion of an iron core of an 11.2 $M_{\odot}$ star is simulated by two-dimensional neutrino-radiation hydrodynamic code. The explosion is driven by the neutrino heating aided by multi-dimensional hydrodynamic effects such as the convection. Following the explosion phase, we continue the simulation focusing on the thermal evolution of the protoneutron star up to $\sim$70 s when the crust of the neutron star is formed using one-dimensional simulation. We find that the crust forms at high-density region ($\rho\sim10^{14}$ g cm$^{-3}$) and it would proceed from inside to outside. This is the first self-consistent simulation that successfully follows from the collapse phase to the protoneutron star cooling phase based on the multi-dimensional hydrodynamic simulation.Comment: 5 pages, 3 figures, with minor corrections; accepted to PASJ Letter

Unravelling the dynamics of online ratings

Online product ratings are an immensely important source of information for consumers and accordingly a strong driver of commerce. Nonetheless, interpreting a particular rating in context can be very challenging. Ratings show significant variation over time, so understanding the reasons behind that variation is important for consumers, platform designers, and product creators. In this paper we contribute a set of tools and results that help shed light on the complexity of ratings dynamics. We consider multiple item types across multiple ratings platforms, and use a interpretable model to decompose ratings in a manner that facilitates comprehensibility. We show that the various kinds of dynamics observed in online ratings are largely understandable as a product of the nature of the ratings platform, the characteristics of the user population, known trends in ratings behavior, and the influence of recommendation systems. Taken together, these results provide a framework for both quantifying and interpreting the factors that drive the dynamics of online ratings.Published versio