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Cognition, Language & Communication'14

Cognition, Language & Communication'14

MSc Brain & Cognitive Science, UvA track Cognitive Science Jelle Zuidema Institute for Logic, Language & Computation zuidema@uva.nl week 4: Artificial Language Learning. Cognition, Language & Communication'14. Recap: Transitional Probabilities. #. A. B. C. D.

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454 views • 44 slides
Neural Models: how structures shape dynamics

Neural Models: how structures shape dynamics

Neural Models: how structures shape dynamics. Vincenzo G. Fiore School of Behavioral and Brain Sciences, UTD. Neuro CPU lab. Index. There is something about the networks Input-output and temporal transformation Feedforward networks: linear dynamics

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434 views • 41 slides
Lecture 6 Smaller Network: RNN

Lecture 6 Smaller Network: RNN

Lecture 6 Smaller Network: RNN. This is our fully connected network. If x 1 . ... x n , n is very large and growing, this network would become too large. We now will input one x i at a time , and re-use the same edge weights. Recurrent Neural Network. How does RNN reduce complexity?.

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505 views • 39 slides
LEARNING SEQUENCES FROM Conway’s game of life

LEARNING SEQUENCES FROM Conway’s game of life

SE367 Project Final Presentation. LEARNING SEQUENCES FROM Conway’s game of life. By: Sujith Thomas Parimi Krishna Chaitanya In charge:- Prof Amitabha Mukerjee. To make a neural net learn the rules of Conway’s game of life and predict the next generation of cells.

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256 views • 13 slides
SESL Meeting / Presentation April 2003

SESL Meeting / Presentation April 2003

SESL Meeting / Presentation April 2003. Takenori Kamo. Currently, working on. Revising the paper, “Global Stock Index Forecasting Using Multiple Generalized Regression Neural Networks with a Gating Network” for the Journal, “ Applied Intelligence”, Kluwer Academic Publishers.

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165 views • 6 slides

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