Inference by Minimizing Size, Divergence, or their Sum

We speed up marginal inference by ignoring factors that do not significantly contribute to overall accuracy. In order to pick a suitable subset of factors to ignore, we propose three schemes: minimizing the number of model factors under a bound on the KL divergence between pruned and full models; minimizing the KL divergence under a bound on factor count; and minimizing the weighted sum of KL divergence and factor count. All three problems are solved using an approximation of the KL divergence than can be calculated in terms of marginals computed on a simple seed graph. Applied to synthetic image denoising and to three different types of NLP parsing models, this technique performs marginal inference up to 11 times faster than loopy BP, with graph sizes reduced up to 98%-at comparable error in marginals and parsing accuracy. We also show that minimizing the weighted sum of divergence and size is substantially faster than minimizing either of the other objectives based on the approximation to divergence presented here.Comment: Appears in Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence (UAI2010

Alien Registration- Mccallum, George A. (Portland, Cumberland County)

https://digitalmaine.com/alien_docs/21484/thumbnail.jp

Epilepsy

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Alien Registration- Mccallum, George A. (Portland, Cumberland County)

https://digitalmaine.com/alien_docs/21484/thumbnail.jp

The effect of low-energy ion-implantation on the electrical transport properties of Si-SiO2 MOSFETs

Using silicon MOSFETs with thin (5nm) thermally grown SiO2 gate dielectrics, we characterize the density of electrically active traps at low-temperature after 16keV phosphorus ion-implantation through the oxide. We find that, after rapid thermal annealing at 1000oC for 5 seconds, each implanted P ion contributes an additional 0.08 plus/minus 0.03 electrically active traps, whilst no increase in the number of traps is seen for comparable silicon implants. This result shows that the additional traps are ionized P donors, and not damage due to the implantation process. We also find, using the room temperature threshold voltage shift, that the electrical activation of donors at an implant density of 2x10^12 cm^-2 is ~100%.Comment: 11 pages, 10 figure

Degrees of Change: Understanding Academics Experiences with a Shift to Flexible Technology-Enhanced Learning in Initial Teacher Education

The implementation of technology enhanced learning in higher education is often associated with changes to academic work. This article reports on a study of staff experiences with curriculum development and teaching in multiple modes of blended and online learning in a Bachelor of Education degree. The findings indicate that the changes experienced by these teacher educators were significant but not wholesale. More specifically, the findings highlight three particular areas of change that impacted on their role as teacher educators: changed pedagogical practices, particularly in staff-student communication, interaction and relationship building with students; increasing workloads associated with flexible delivery; and changed needs for staff capacity building related to issues of quality in technology enhanced learning

On Horizontal and Vertical Separation in Hierarchical Text Classification

Hierarchy is a common and effective way of organizing data and representing their relationships at different levels of abstraction. However, hierarchical data dependencies cause difficulties in the estimation of "separable" models that can distinguish between the entities in the hierarchy. Extracting separable models of hierarchical entities requires us to take their relative position into account and to consider the different types of dependencies in the hierarchy. In this paper, we present an investigation of the effect of separability in text-based entity classification and argue that in hierarchical classification, a separation property should be established between entities not only in the same layer, but also in different layers. Our main findings are the followings. First, we analyse the importance of separability on the data representation in the task of classification and based on that, we introduce a "Strong Separation Principle" for optimizing expected effectiveness of classifiers decision based on separation property. Second, we present Hierarchical Significant Words Language Models (HSWLM) which capture all, and only, the essential features of hierarchical entities according to their relative position in the hierarchy resulting in horizontally and vertically separable models. Third, we validate our claims on real-world data and demonstrate that how HSWLM improves the accuracy of classification and how it provides transferable models over time. Although discussions in this paper focus on the classification problem, the models are applicable to any information access tasks on data that has, or can be mapped to, a hierarchical structure.Comment: Full paper (10 pages) accepted for publication in proceedings of ACM SIGIR International Conference on the Theory of Information Retrieval (ICTIR'16

Universal schema for entity type prediction

Categorizing entities by their types is useful in many applications, including knowledge base construction, relation extraction and query intent prediction. Fine-grained entity type ontologies are especially valuable, but typically difficult to design because of unavoidable quandaries about level of detail and boundary cases. Automatically classifying entities by type is challenging as well, usually involving hand-labeling data and training a supervised predictor. This paper presents a universal schema approach to fine-grained entity type prediction. The set of types is taken as the union of textual surface patterns (e.g. appositives) and pre-defined types from available databases (e.g. Freebase) - yielding not tens or hundreds of types, but more than ten thousands of entity types, such as financier, criminologist, and musical trio. We robustly learn mutual implication among this large union by learning latent vector embeddings from probabilistic matrix factorization, thus avoiding the need for hand-labeled data. Experimental results demonstrate more than 30% reduction in error versus a traditional classification approach on predicting fine-grained entities types. © 2013 ACM