Most web sites today are designed one-size-fits-all: all visitors see the exact same pages
regardless of interests, previous interactions, or, frequently, even browsing client (desktop …

Temporal interpolation is the task of bridging gaps between time-oriented concepts in a
context-sensitive manner. It is a subtask important for solving the temporal-abstraction task …

Current technology for automatic speech recognition (ASR) uses hidden Markov models
(HMMs) that recognize spoken speech using the acoustic signal. However, no use is made …

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This dissertation describes a reasoning framework for knowledge-based systems, specific to
the task of abstracting higher-level, interval-based concepts from time-stamped data, but …

In this paper, we show a new approach for reasoning about time and probability that
combines a formal declarative language with a graph representation of systems of random …

We propose a novel approach toward event detection in real-world continuous video
sequences. The method: 1) is able to model arbitrary-order non-Markovian dependences in …

In this paper, we propose a methodology for using dynamic Bayesian networks (DBN) in the
tasks of assessing the success of an investment project. The methods of constructing DBN …

This paper describes the application of Bayesian networks to automatic speech recognition
(ASR). Bayesian networks enable the construction of probabilistic models in which an …

Many real world applications depend on modeling the temporal dynamics of streams of
diverse events, many of which are rare. We introduce a novel model class, Conjoint …