Forgetting Exceptions is Harmful in Language Learning

We show that in language learning, contrary to received wisdom, keeping exceptional training instances in memory can be beneficial for generalization accuracy. We investigate this phenomenon empirically on a selection of benchmark natural language processing tasks: grapheme-to-phoneme conversion, part-of-speech tagging, prepositional-phrase attachment, and base noun phrase chunking. In a first series of experiments we combine memory-based learning with training set editing techniques, in which instances are edited based on their typicality and class prediction strength. Results show that editing exceptional instances (with low typicality or low class prediction strength) tends to harm generalization accuracy. In a second series of experiments we compare memory-based learning and decision-tree learning methods on the same selection of tasks, and find that decision-tree learning often performs worse than memory-based learning. Moreover, the decrease in performance can be linked to the degree of abstraction from exceptions (i.e., pruning or eagerness). We provide explanations for both results in terms of the properties of the natural language processing tasks and the learning algorithms.Comment: 31 pages, 7 figures, 10 tables. uses 11pt, fullname, a4wide tex styles. Pre-print version of article to appear in Machine Learning 11:1-3, Special Issue on Natural Language Learning. Figures on page 22 slightly compressed to avoid page overloa

Chances, counterfactuals and similarity

John Hawthorne in a recent paper takes issue with Lewisian accounts of counterfactuals, when relevant laws of nature are chancy. I respond to his arguments on behalf of the Lewisian, and conclude that while some can be rebutted, the case against the original Lewisian account is strong. I develop a neo-Lewisian account of what makes for closeness of worlds. I argue that my revised version avoids Hawthorne’s challenges. I argue that this is closer to the spirit of Lewis’s first (non-chancy) proposal than is Lewis’s own suggested modification

Structured computer-based training in the interpretation of neuroradiological images

Computer-based systems may be able to address a recognised need throughout the medical profession for a more structured approach to training. We describe a combined training system for neuroradiology, the MR Tutor that differs from previous approaches to computer-assisted training in radiology in that it provides case-based tuition whereby the system and user communicate in terms of a well-founded Image Description Language. The system implements a novel method of visualisation and interaction with a library of fully described cases utilising statistical models of similarity, typicality and disease categorisation of cases. We describe the rationale, knowledge representation and design of the system, and provide a formative evaluation of its usability and effectiveness

Toward a Taxonomy and Computational Models of Abnormalities in Images

The human visual system can spot an abnormal image, and reason about what makes it strange. This task has not received enough attention in computer vision. In this paper we study various types of atypicalities in images in a more comprehensive way than has been done before. We propose a new dataset of abnormal images showing a wide range of atypicalities. We design human subject experiments to discover a coarse taxonomy of the reasons for abnormality. Our experiments reveal three major categories of abnormality: object-centric, scene-centric, and contextual. Based on this taxonomy, we propose a comprehensive computational model that can predict all different types of abnormality in images and outperform prior arts in abnormality recognition.Comment: To appear in the Thirtieth AAAI Conference on Artificial Intelligence (AAAI 2016

How to choose the endorser: An experimental analysis on the effects of fit and notoriety

The present study is focused on the endorser topic following two different paths: firstly, proposing an extension of the theoretical match-up model, enlarge it through two other potential types of consistency: the typicality fit and the imagery fit. Secondly, the present study aims verifies the applicability of the same framework to the emerging situation with a brand linked to a not well-known endorser (internal as the founder or external as a web influencer). An experimental 3*2 (fit typology*high/low notoriety) between subject analysis was conducted in the food service domain. It showed some interesting considerations.From a theoretical point of view, the first relevant finding is that endorsement might be assimilated to a co-branding strategy, confirming the match-up model as an effective theoretical framework in this domain as well, with significant differences among the three fit typologies investigated. The typicality fit reveals to be the less effective in increasing attitude and other behavioural effects on consumers in spite of the large adoption of this kind of fit by companies. Instead, the imagery fit, seems to be the most impactful in terms of positive word of mouth activation and viral communication activities, at the same level at the categorical one. Moreover, the categorical fit induces the wider range of positive effect on the dependent variables (attitudes, willingness to pay and willingness to buy). Another interesting contribution is that the presence of an appropriate fit (in particular the categorical one) is able to compensate the absence of endorser notoriety and, on the average, the usage of a very popular endorser from the same domain of the brand is not necessary more effective in comparison with a not well-known endorser form the same domain. This result is the peak of the present research from a managerial point of view, as it leads to consider the opportunity to support the emerging practices by which companies turn to not well-known people (disclosing the founder, or presenting some workers, or adopting a common consumer as an influencer). The endorser not well-known, but presented with an adequate story-telling might be the best choice: less onerous and more effective than a big unrelated celebrity

Accounting for Graded Performance within a Discrete Search Framework

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/98271/1/s15516709cog2004_2.pd

Statistical Predictions From Anarchic Field Theory Landscapes

Consistent coupling of effective field theories with a quantum theory of gravity appears to require bounds on the the rank of the gauge group and the amount of matter. We consider landscapes of field theories subject to such to boundedness constraints. We argue that appropriately "coarse-grained" aspects of the randomly chosen field theory in such landscapes, such as the fraction of gauge groups with ranks in a given range, can be statistically predictable. To illustrate our point we show how the uniform measures on simple classes of N=1 quiver gauge theories localize in the vicinity of theories with certain typical structures. Generically, this approach would predict a high energy theory with very many gauge factors, with the high rank factors largely decoupled from the low rank factors if we require asymptotic freedom for the latter.Comment: 22 pages, 5 figure