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Speaker: Chris Eliasmith

I present a cognitive model, dubbed BioSLIE, that integrates and extends recent advances in:

  • distributed, structure sensitive representation
  • neurocomputational modeling
  • our understanding of the neuroanatomy of linguistic inference
Friday, March 24, 2006 11:30 am - 11:30 am EST (GMT -05:00)

AI seminar: Semi-supervised multi-view HMMs learning for information extraction

Speaker: Zhenmei Gu

In information extraction (IE), training statistical models like HMMs (hidden Markov models) usually requires a considerably large set of labeled data. Such a requirement may not be easily met in a practical IE application. In this talk, we investigate how to adapt a fully supervised IE learner to a semi-supervised one so that the learner is able to make use of unlabeled data to help train a more robust model from very limited labeled data.

Speaker: Jane Morris

Most research on automated text understanding, including applications in information retrieval and text summarization, does not consider the potential subjectivity of human interpretation of text. In particular, the subjectivity of the linguistic theory of lexical cohesion that has been used in these applications has not been examined empirically.

Friday, April 7, 2006 11:30 am - 11:30 am EDT (GMT -04:00)

AI seminar: Optical snow

Speaker: Richard Mann

Classical methods for measuring image motion by computer have concentrated on the cases of optical flow in which the motion field is continuous, or layered motion in which the motion field is piecewise continuous. Here we introduce a third natural category which we call optical snow.

Speaker: Jie Zhang

In multiagent systems populated by self-interested agents, consumer agents would benefit by modeling the reputation of provider agents, in order to make effective decisions about which agents to trust. One method for representing reputation is to ask other agents in the system (called advisor agents) to provide ratings of the provider agents.

Speaker: Georgia Kastidou

In this paper, we present a model for delivering personalized ads to users while they are watching TV shows. Our approach is to model user preferences, based on characterizing not only the keywords of primary interest but also the relative weighting of those keywords.

Friday, May 19, 2006 11:30 am - 11:30 am EDT (GMT -04:00)

AI seminar: Trajectory capture in frontal plane geometry for visually impaired

Speaker: Martin Talbot

Users who are blind, or whose visual attention is otherwise occupied, can benefit from an auditory representation of their immediate environment. To create it a video camera senses the environment, which is converted into synthetic audio streams that represent objects in the environment. What aspects of the audio signal best encode this information?