10,846 research outputs found
The Doctrine of the Implied Insured in Alaska: Recent Developments Concerning Third Parties to Insurance Contracts
Extracting low-dimensional psychological representations from convolutional neural networks
Deep neural networks are increasingly being used in cognitive modeling as a
means of deriving representations for complex stimuli such as images. While the
predictive power of these networks is high, it is often not clear whether they
also offer useful explanations of the task at hand. Convolutional neural
network representations have been shown to be predictive of human similarity
judgments for images after appropriate adaptation. However, these
high-dimensional representations are difficult to interpret. Here we present a
method for reducing these representations to a low-dimensional space which is
still predictive of similarity judgments. We show that these low-dimensional
representations also provide insightful explanations of factors underlying
human similarity judgments.Comment: Accepted to CogSci 202
Modeling Human Categorization of Natural Images Using Deep Feature Representations
Over the last few decades, psychologists have developed sophisticated formal
models of human categorization using simple artificial stimuli. In this paper,
we use modern machine learning methods to extend this work into the realm of
naturalistic stimuli, enabling human categorization to be studied over the
complex visual domain in which it evolved and developed. We show that
representations derived from a convolutional neural network can be used to
model behavior over a database of >300,000 human natural image classifications,
and find that a group of models based on these representations perform well,
near the reliability of human judgments. Interestingly, this group includes
both exemplar and prototype models, contrasting with the dominance of exemplar
models in previous work. We are able to improve the performance of the
remaining models by preprocessing neural network representations to more
closely capture human similarity judgments.Comment: 13 pages, 7 figures, 6 tables. Preliminary work presented at CogSci
201
Learning a face space for experiments on human identity
Generative models of human identity and appearance have broad applicability
to behavioral science and technology, but the exquisite sensitivity of human
face perception means that their utility hinges on the alignment of the model's
representation to human psychological representations and the photorealism of
the generated images. Meeting these requirements is an exacting task, and
existing models of human identity and appearance are often unworkably abstract,
artificial, uncanny, or biased. Here, we use a variational autoencoder with an
autoregressive decoder to learn a face space from a uniquely diverse dataset of
portraits that control much of the variation irrelevant to human identity and
appearance. Our method generates photorealistic portraits of fictive identities
with a smooth, navigable latent space. We validate our model's alignment with
human sensitivities by introducing a psychophysical Turing test for images,
which humans mostly fail. Lastly, we demonstrate an initial application of our
model to the problem of fast search in mental space to obtain detailed "police
sketches" in a small number of trials.Comment: 10 figures. Accepted as a paper to the 40th Annual Meeting of the
Cognitive Science Society (CogSci 2018). *JWS and JCP contributed equally to
this submissio
Regional carbon dioxide permit trading in the United States: coalition choices for Pennsylvania
An overview is given of the growing number of regional associations in which states have entered into voluntary arrangements to limit greenhouse gas (GHG) emissions. In particular, in the Regional Greenhouse Gas Initiative (RGGI), a number of northeastern states have joined to create a regional GHG cap and trade program, beginning with the utility industry. Analysis is made of the five key issues relating to these current and potential climate action associations: the extent of the total and individual state mitigation cost-savings across all sectors from potential emission permit trading coalitions; the size of permit markets associated with the various coalitions; the relative advantages of joining various coalitions for swing states such as Pennsylvania; the implications of the exercise of market power in the permit market; and the total and individual state/country cost-savings from extending the coalition beyond US borders. It is shown that overall efficiency gains from trading with a system of flexible state caps, with greater overall cost savings increasing with increasing geographic scope.Regional Greenhouse Gas Initiative; Cap and trade program; Market power in the permit market; Mitigation costs; The size of permit market; Coalition choices for Pennsylvania
Examining Loss of Soul in Education
One does not have to walk long in the hallways of schools to see that something is very wrong. We search in our students for the passion and enthusiasm for learning that they possessed before entering school. Instead, we find idle bystanders content to remain distant from formal instruction and from their hearts. Too often they remain dazed, passive consumers window shopping the mall of education. Education becomes something to get through with a grade or a degree rather than a clearing for deep experience. A common malady of education today is the familiar emptiness found in the experiences of both teachers and students
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