Variational Inference for Logical Inference

Functional Distributional Semantics is a framework that aims to learn, from text, semantic representations which can be interpreted in terms of truth. Here we make two contributions to this framework. The first is to show how a type of logical inference can be performed by evaluating conditional probabilities. The second is to make these calculations tractable by means of a variational approximation. This approximation also enables faster convergence during training, allowing us to close the gap with state-of-the-art vector space models when evaluating on semantic similarity. We demonstrate promising performance on two tasks.Schiff Foundatio

Semantic Composition via Probabilistic Model Theory

Semantic composition remains an open problem for vector space models of semantics. In this paper, we explain how the probabilistic graphical model used in the framework of Functional Distributional Semantics can be interpreted as a probabilistic version of model theory. Building on this, we explain how various semantic phenomena can be recast in terms of conditional probabilities in the graphical model. This connection between formal semantics and machine learning is helpful in both directions: it gives us an explicit mechanism for modelling context-dependent meanings (a challenge for formal semantics), and also gives us well-motivated techniques for composing distributed representations (a challenge for distributional semantics). We present results on two datasets that go beyond word similarity, showing how these semantically-motivated techniques improve on the performance of vector models.Schiff Foundatio

The Safety Challenges of Deep Learning in Real-World Type 1 Diabetes Management

Blood glucose simulation allows the effectiveness of type 1 diabetes (T1D) management strategies to be evaluated without patient harm. Deep learning algorithms provide a promising avenue for extending simulator capabilities; however, these algorithms are limited in that they do not necessarily learn physiologically correct glucose dynamics and can learn incorrect and potentially dangerous relationships from confounders in training data. This is likely to be more important in real-world scenarios, as data is not collected under strict research protocol. This work explores the implications of using deep learning algorithms trained on real-world data to model glucose dynamics. Free-living data was processed from the OpenAPS Data Commons and supplemented with patient-reported tags of challenging diabetes events, constituting one of the most detailed real-world T1D datasets. This dataset was used to train and evaluate state-of-the-art glucose simulators, comparing their prediction error across safety critical scenarios and assessing the physiological appropriateness of the learned dynamics using Shapley Additive Explanations (SHAP). While deep learning prediction accuracy surpassed the widely-used mathematical simulator approach, the model deteriorated in safety critical scenarios and struggled to leverage self-reported meal and exercise information. SHAP value analysis also indicated the model had fundamentally confused the roles of insulin and carbohydrates, which is one of the most basic T1D management principles. This work highlights the importance of considering physiological appropriateness when using deep learning to model real-world systems in T1D and healthcare more broadly, and provides recommendations for building models that are robust to real-world data constraints.Comment: 15 pages, 3 figure

Por qué no les calan? Hugo Chávez’s Re-election in Venezuela and the Decline of Western Hegemony in the Americas

On October 7, 2012, Hugo Chávez was comfortably re-elected president of Venezuela. Just days before the vote the impression given by major international print media was that the vote was a close-run thing, an assessment which proved to be at best optimistic. We argue that Western media coverage of the election in Venezuela was designed to skew the result towards the opposition and that these efforts singularly failed. The conclusions of our analysis are, first, that the “propaganda” model advanced by Chomsky is now faltering in the Americas and, second, that the region is acting in manner that is increasingly free of influence from the US. Venezuela thus stands as a case of the citizenry of a country actively and independently asserting its political agency despite clear attempts to redirect its thinking and decision-making

Functional Distributional Semantics: Learning Linguistically Informed Representations from a Precisely Annotated Corpus

The aim of distributional semantics is to design computational techniques that can automatically learn the meanings of words from a body of text. The twin challenges are: how do we represent meaning, and how do we learn these representations? The current state of the art is to represent meanings as vectors – but vectors do not correspond to any traditional notion of meaning. In particular, there is no way to talk about truth, a crucial concept in logic and formal semantics. In this thesis, I develop a framework for distributional semantics which answers this challenge. The meaning of a word is not represented as a vector, but as a function, mapping entities (objects in the world) to probabilities of truth (the probability that the word is true of the entity). Such a function can be interpreted both in the machine learning sense of a classifier, and in the formal semantic sense of a truth-conditional function. This simultaneously allows both the use of machine learning techniques to exploit large datasets, and also the use of formal semantic techniques to manipulate the learnt representations. I define a probabilistic graphical model, which incorporates a probabilistic generalisation of model theory (allowing a strong connection with formal semantics), and which generates semantic dependency graphs (allowing it to be trained on a corpus). This graphical model provides a natural way to model logical inference, semantic composition, and context-dependent meanings, where Bayesian inference plays a crucial role. I demonstrate the feasibility of this approach by training a model on WikiWoods, a parsed version of the English Wikipedia, and evaluating it on three tasks. The results indicate that the model can learn information not captured by vector space models.Schiff Fund Studentshi

Biopolitics and Necropolitics on the Northern Border

Este artículo explora el funcionamiento de la seguridad ciudadana. Se desarrolla haciendo referencia a las iniciativas en Tijuana, México, y revela cómo la seguridad ciudadana opera a través de tecnologías que, simultáneamente, generan tanto ciudadanos compatibles, como no-ciudadanos abyectos. Ambos grupos se vuelven parte integral de la seguridad ciudadana y funcionan dentro de una lógica a través de la que se intenta asegurar la vida de los primeros (la biopolítica) y se genera la exclusión de los segundos (la necropolítica). Desde la biopolítica, la población es entendida a través de medidas de vigilancia que permiten la acumulación de información y el análisis de datos. Estos conciernen a los patrones de comportamiento observados dentro de esta población, con el objetivo de gestionar eventos aleatorios y poblaciones fuera de lo que es empíricamente normal. Estas poblaciones anormales son entonces reguladas a través de iniciativas compuestas por el Programa Nacional de Prevención del Delito (PRONAPRED), con el propósito de darles ciertas capacidades de vida para superar sus situaciones de riesgo. Por su parte, los no-ciudadanos son tratados con base a una inclusión excluyente (la necropolítica). En la práctica, esto implica que la población no-ciudadana sea vista como un objeto de vigilancia jerárquica. Ésta última está diseñada para regular el movimiento y limitar el peligro que estos no-ciudadanos representan para la seguridad pública. De manera similar, los individuos no-ciudadanos son marginados en la medida en la que su inclusión en los programas de seguridad del PRONAPRED, hace posible su eventual expulsión de México. Es así que el artículo concluye que la seguridad ciudadana determina y exacerba la distinción ciudadano/no-ciudadano, una distinción que puede significar la vida o la muerte.This article explores the functioning of citizen security. It is developed with reference to the initiatives in Tijuana, Mexico, and reveals how citizen security operates through technologies that simultaneously generate both compatible citizens and abject non-citizens. Both groups become an integral part of citizen security and operate within a logic through which one tries to ensure the life of the former (biopolitics) and the exclusion of the latter is generated (the necropolitics). From biopolitics, the population is understood through surveillance measures that allow the accumulation of information and the analysis of data. These concern the observed patterns of behavior within this population, with the aim of managing random events and populations outside of what is empirically normal. These abnormal populations are then regulated through initiatives composed of the National Program for the Prevention of Crime (PRONAPRED), with the purpose of giving them certain life skills to overcome their risk situations. On the other hand, non-citizens are treated based on an exclusionary inclusion (the necropolitics). In practice, this implies that the non-citizen population is seen as an object of hierarchical surveillance. The latter is designed to regulate movement and limit the danger that these non-citizens represent for public safety. Similarly, non-citizen individuals are marginalized to the extent that their inclusion in the PRONAPRED security programs makes possible their eventual expulsion from Mexico. Thus, the article concludes that citizen security determines and exacerbates the citizen / non-citizen distinction, a distinction that can mean life or death