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Now showing items 141-150 of 388
Virtual Model Control of a Biped Walking Robot
(1995-12-01)
The transformation from high level task specification to low level motion control is a fundamental issue in sensorimotor control in animals and robots. This thesis develops a control scheme called virtual model control ...
Automated Reasoning About Classical Mechanics
(1994-05-01)
In recent years, researchers in artificial intelligence have become interested in replicating human physical reasoning talents in computers. One of the most important skills in this area is predicting how physical ...
Probabilistic Independence Networks for Hidden Markov Probability Models
(1996-03-13)
Graphical techniques for modeling the dependencies of randomvariables have been explored in a variety of different areas includingstatistics, statistical physics, artificial intelligence, speech recognition, image ...
Factorial Hidden Markov Models
(1996-02-09)
We present a framework for learning in hidden Markov models with distributed state representations. Within this framework, we derive a learning algorithm based on the Expectation--Maximization (EM) procedure for maximum ...
Fast Learning by Bounding Likelihoods in Sigmoid Type Belief Networks
(1996-02-09)
Sigmoid type belief networks, a class of probabilistic neural networks, provide a natural framework for compactly representing probabilistic information in a variety of unsupervised and supervised learning problems. ...
Image Based Rendering Using Algebraic Techniques
(1996-11-01)
This paper presents an image-based rendering system using algebraic relations between different views of an object. The system uses pictures of an object taken from known positions. Given three such images it can ...
Model-Based Matching by Linear Combinations of Prototypes
(1996-12-01)
We describe a method for modeling object classes (such as faces) using 2D example images and an algorithm for matching a model to a novel image. The object class models are "learned'' from example images that we call ...
Model-Based Matching of Line Drawings by Linear Combinations of Prototypes
(1996-01-18)
We describe a technique for finding pixelwise correspondences between two images by using models of objects of the same class to guide the search. The object models are 'learned' from example images (also called ...
Neural Networks
(1996-03-13)
We present an overview of current research on artificial neural networks, emphasizing a statistical perspective. We view neural networks as parameterized graphs that make probabilistic assumptions about data, and view ...
Learning Linear, Sparse, Factorial Codes
(1996-12-01)
In previous work (Olshausen & Field 1996), an algorithm was described for learning linear sparse codes which, when trained on natural images, produces a set of basis functions that are spatially localized, oriented, ...