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Physical Sciences and Mathematics Commons™
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Articles 1 - 6 of 6
Full-Text Articles in Physical Sciences and Mathematics
Improving The Separability Of A Reservoir Facilitates Learning Transfer, David Norton, Dan A. Ventura
Improving The Separability Of A Reservoir Facilitates Learning Transfer, David Norton, Dan A. Ventura
Faculty Publications
We use a type of reservoir computing called the liquid state machine (LSM) to explore learning transfer. The Liquid State Machine (LSM) is a neural network model that uses a reservoir of recurrent spiking neurons as a filter for a readout function. We develop a method of training the reservoir, or liquid, that is not driven by residual error. Instead, the liquid is evaluated based on its ability to separate different classes of input into different spatial patterns of neural activity. Using this method, we train liquids on two qualitatively different types of artificial problems. Resulting liquids are shown to …
Edge Inference For Image Interpolation, Bryan S. Morse, Neil Toronto, Dan A. Ventura
Edge Inference For Image Interpolation, Bryan S. Morse, Neil Toronto, Dan A. Ventura
Faculty Publications
Image interpolation algorithms try to fit a function to a matrix of samples in a "natural-looking" way. This paper presents edge inference, an algorithm that does this by mixing neural network regression with standard image interpolation techniques. Results on gray level images are presented, and it is demonstrated that edge inference is capable of producing sharp, natural-looking results. A technique for reintroducing noise is given, and it is shown that, with noise added using a bicubic interpolant, edge inference can be regarded as a generalization of bicubic interpolation. Extension into RGB color space and additional applications of the algorithm are …
Feature Weighting Using Neural Networks, Tony R. Martinez, Xinchuan Zeng
Feature Weighting Using Neural Networks, Tony R. Martinez, Xinchuan Zeng
Faculty Publications
In this work we propose a feature weighting method for classification tasks by extracting relevant information from a trained neural network. This method weights an attribute based on strengths (weights) of related links in the neural network, in which an important feature is typically connected to strong links and has more impact on the outputs. This method is applied to feature weighting br the nearest neighbor classifier and is tested on 15 real-world classification tasks. The results show that it can improve the nearest neighbor classifier on 14 of the 15 tested tasks, and also outperforms the neural network on …
Learning Multiple Correct Classifications From Incomplete Data Using Weakened Implicit Negatives, Dan A. Ventura, Stephen Whiting
Learning Multiple Correct Classifications From Incomplete Data Using Weakened Implicit Negatives, Dan A. Ventura, Stephen Whiting
Faculty Publications
Classification problems with output class overlap create problems for standard neural network approaches. We present a modification of a simple feed-forward neural network that is capable of learning problems with output overlap, including problems exhibiting hierarchical class structures in the output. Our method of applying weakened implicit negatives to address overlap and ambiguity allows the algorithm to learn a large portion of the hierarchical structure from very incomplete data. Our results show an improvement of approximately 58% over a standard backpropagation network on the hierarchical problem.
Optically Simulating A Quantum Associative Memory, Dan A. Ventura, John C. Howell, John A. Yeazell
Optically Simulating A Quantum Associative Memory, Dan A. Ventura, John C. Howell, John A. Yeazell
Faculty Publications
This paper discusses the realization of a quantum associative memory using linear integrated optics. An associative memory produces a full pattern of bits when presented with only a partial pattern. Quantum computers have the potential to store large numbers of patterns and hence have the ability to far surpass any classical neural network realization of an associative memory. In this work two 3-qubit associative memories will be discussed using linear integrated optics. In addition, corrupted, invented and degenerate memories are discussed.
Optimal Control Using A Neural/Evolutionary Hybrid System, Tony R. Martinez, Dan A. Ventura
Optimal Control Using A Neural/Evolutionary Hybrid System, Tony R. Martinez, Dan A. Ventura
Faculty Publications
One of the biggest hurdles to developing neurocontrollers is the difficulty in establishing good training data for the neural network. We propose a hybrid approach to the development of neurocontrollers that employs both evolutionary computation (EC) and neural networks (NN). EC is used to discover appropriate control actions for specific plant states. The survivors of the evolutionary process are used to construct a training set for the NN. The NN leams the training set, is able to generalize to new plant states, and is then used for neurocontrol. Thus the EC/NN approach combines the broad, parallel search of EC with …