Publications
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Multiple-State Context-Dependent Phonetic Modeling with MLPs
In this paper we present a new MLP architecture and training procedure for modeling context-dependent phonetic classes with a sequence of distributions.
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Camera self-calibration: theory and experiments
In this paper a complete method for calibrating a camera is presented. In contrast with existing methods it does not require a calibration object with a known 3D shape.
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Attachment Methods for Integration
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Reasoning with Analogical Representations
The framework consists of a set of generic operations on analogical structures and accompanying inference methods for integrating analogical and sentential information.
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Active head movements help solve stereo correspondance
Keywords: Artificial Intelligence, Artificial Intelligence Center, AIC
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Active Stereo with head movement
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Combining Neural Networks and Hidden Markov Models for Continuous Speech Recognition
We present a speaker-independent, continuous-speech recognition system based on a hybrid multilayer perceptron (MLP)/hidden Markov model (HMM). The system combines the advantages of both approaches by using MLPs to estimate…
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The Planning of Actions and The Basal Ganglia
The model posits that the basal ganglia are responsible for driving smooth transitions of state (e.g., joint positions) for an organism.
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Self-calibration of a camera using multiples images
The authors present a complete method for calibrating a camera, which requires only point matches from image sequences.
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Application of Artificial Intelligence to the DOD Directory
The functional and operational requirements that define the DoD Directory will yield a system with a significant level of complexity. We describe the artificial intelligence-based approach developed to solve and…
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A knowledge base of the chemical compounds of intermediary metabolism
This paper describes a publicly available knowledge base of the chemical compounds involved in intermediary metabolism.
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Integrating Neural Networks into Computer Speech Recognition Systems
The work described here involved integrating neural networks into a hidden Markov model-based state-of-the-art continuous-speech recognition system, resulting in improvements in recognition accuracy and reductions in model complexity.