Author name cluster

Michael Coen

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

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AAAI Conference 2014 Conference Paper

A Spatially Sensitive Kernel to Predict Cognitive Performance from Short-Term Changes in Neural Structure

M. Hidayath Ansari
Michael Coen
Barbara Bendlin
Mark Sager
Sterling Johnson

This paper introduces a novel framework for performing machine learning on longitudinal neuroimaging datasets. These datasets are characterized by their size, particularly their width (millions of features per data input). Specifically, we address the problem of detecting subtle, short-term changes in neural structure that are indicative of cognitive change and correlate with risk factors for Alzheimer’s disease. We introduce a new spatially-sensitive kernel that allows us to reason about individuals, as opposed to populations. In doing so, this paper presents the first evidence demonstrating that very small changes in white matter structure over a two year period can predict change in cognitive function in healthy adults.

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AAAI Conference 2011 Conference Paper

Learning from Spatial Overlap

Michael Coen
M. Ansari
Nathanael Fillmore

This paper explores a new measure of similarity between point sets in arbitrary metric spaces. The measure is based on the spatial overlap of the shapes and densities of these point sets. It is applicable in any domain where point sets are a natural representation for data. Speciﬁcally, we show examples of its use in natural language processing, object recognition in images, and multidimensional point set classiﬁcation. We provide a geometric interpretation of this measure and show that it is well-motivated, intuitive, parameter-free, and straightforward to use. We further demonstrate that it is computationally tractable and applicable to both supervised and unsupervised learning problems.

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AAAI Conference 2006 Conference Paper

Self-Supervised Acquisition of Vowels in American English

Michael Coen

This paper presents a self-supervised framework for perceptual learning based upon correlations in different sensory modalities. We demonstrate this with a system that has learned the vowel structure of American English – i. e. , the number of vowels and their phonetic descriptions – by simultaneously watching and listening to someone speak. It is highly non-parametric, knowing neither the number of vowels nor their input distributions in advance, and it has no prior linguistic knowledge. This work is the first example of unsupervised phonetic acquisition of which we are aware, outside of that done by human infants. This system is based on the cross-modal clustering framework introduced by [4], which has been significantly enhanced here. This paper presents our results and focuses on the mathematical framework that enables this type of intersensory selfsupervised learning.

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AAAI Conference 1994 Conference Paper

An Experiment in the Design of Software Agents

Henry Kautz
Michael Coen

We describe a bottom-up approach to the design of software agents. We built and tested an agent system that addresses the real-world problem of handling the activities involved in scheduling a visitor to our laboratory. The system employs both task-specific and user-centered agents, and communicates with users using both email and a graphical interface. This experiment has helped us to identify crucial requirements in the successful deployment of software agents, including issues of reliability, security, and ease of use. The architecture we developed to meet these requirements is flexible and extensible, and is guiding our current research on principles of agent design.

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