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Neural Networks 

Jordan, Michael I.; Bishop, Christopher M. (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 ...
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Fast Learning by Bounding Likelihoods in Sigmoid Type Belief Networks 

Jaakkola, Tommi S.; Saul, Lawrence K.; Jordan, Michael I. (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. ...
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Pre-Attentive Segmentation in the Primary Visual Cortex 

Li, Zhaoping (1998-06-30)
Stimuli outside classical receptive fields have been shown to exert significant influence over the activities of neurons in primary visual cortexWe propose that contextual influences are used for pre-attentive visual ...
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Probabilistic Independence Networks for Hidden Markov Probability Models 

Smyth, Padhraic; Heckerman, David; Jordan, Michael (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 ...
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Active Learning with Statistical Models 

Cohn, David A.; Ghahramani, Zoubin; Jordan, Michael I. (1995-03-21)
For many types of learners one can compute the statistically 'optimal' way to select data. We review how these techniques have been used with feedforward neural networks. We then show how the same principles may be ...
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A Note on Object Class Representation and Categorical Perception 

Riesenhuber, Maximilian; Poggio, Tomaso (1999-12-17)
We present a novel scheme ("Categorical Basis Functions", CBF) for object class representation in the brain and contrast it to the "Chorus of Prototypes" scheme recently proposed by Edelman. The power and flexibility ...
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Model-Based Matching by Linear Combinations of Prototypes 

Jones, Michael J.; Poggio, Tomaso (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 ...
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Cooperative Physics of Fly Swarms: An Emergent Behavior 

Poggio, M.; Poggio, T. (1995-04-11)
We have simulated the behavior of several artificial flies, interacting visually with each other. Each fly is described by a simple tracking system (Poggio and Reichardt, 1973; Land and Collett, 1974) which summarizes ...
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Trans-membrane Signal Transduction and Biochemical Turing Pattern Formation 

Millonas, Mark M.; Rauch, Erik M. (1999-09-28)
The Turing mechanism for the production of a broken spatial symmetry in an initially homogeneous system of reacting and diffusing substances has attracted much interest as a potential model for certain aspects of ...
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Edge and Mean Based Image Compression 

Desai, Ujjaval Y.; Mizuki, Marcelo M.; Masaki, Ichiro; Horn, Berthold K.P. (1996-11-01)
In this paper, we present a static image compression algorithm for very low bit rate applications. The algorithm reduces spatial redundancy present in images by extracting and encoding edge and mean information. Since ...
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AuthorPoggio, Tomaso (6)Jordan, Michael I. (5)Blumberg, Andrew Justin (2)Ghahramani, Zoubin (2)Hearn, Robert A. (2)Jones, Michael J. (2)Lozano-Perez, Tomas (2)Mellor, J.P. (2)Nagao, Kenji (2)Nagpal, Radhika (2)... View MoreSubject
AI (53)
Artificial Intelligence (53)
MIT (51)Computer Vision (5)pattern recognition (4)EM algorithm (3)neural networks (3)robotics (3)3D Reconstruction (2)car detection (2)... View MoreDate Issued1996 (22)1999 (11)1995 (8)1998 (5)1997 (2)2000 (2)2004 (2)1994 (1)Has File(s)Yes (53)

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