Applying state of the art deeplearning models to novel real world datasets gives a practical evaluation of the generalizability of these models. Of importance in this process is how sensitive the hyper parameters of such models are to novel datasets as this would affect the reproducibility of a model. We present work to characterize the hyper parameter space of an LSTM for language modeling on ...

In his elegant discussion, Sripada distinguishes three possible innate bases for aspects of morality: (1) certain specific principles might be innate, (2) a less simple “principles and parameters” model might apply, and (3) innate biases might have have some influence over what morality a person acquires without determining the content of that morality.1 He argues against (1) and (2) and in fav...

The use of auctions for distributing resources in competing environments has produced a large variety of auctions types and algorithms to treat them. However, auctions have some problems when faced with some real-world applications containing renewable and perishable resources. In this paper we present a mechanism to deal with such issues by dynamically configuring some of the auction parameter...

When modeling student learning, tutors that use the Knowledge Tracing framework often assume that all students have the same set of model parameters. We find that when fitting parameters to individual students, there is significant variation among the individual’s parameters. We examine if this variation is important in terms of instructional decisions by computing the difference in the expecte...