Annotated List of Publications

Journal Articles

Ventura, Dan, "Quantum Computational Intelligence: Answers and Questions", IEEE Intelligent Systems, vol. 14 no. 4, pp. 14-16, July/August 1999.

Abstract:

This is a brief article discussing the interesting possibilities and potential difficulties with combining classical computational intelligence with quantum computation.

Ventura, Dan and Tony Martinez, "A Quantum Computational Learning Algorithm", submitted to Neural Computation, January 1999.

Abstract:

Ventura, Dan and Tony Martinez, "Quantum Associative Memory", Information Sciences, to appear, 1999.

Abstract:

This paper combines quantum computation with classical neural network theory to produce a quantum computational learning algorithm. Quantum computation uses microscopic quantum level effects to perform computational tasks and has produced results that in some cases are exponentially faster than their classical counterparts. The unique characteristics of quantum theory may also be used to create a quantum associative memory with a capacity exponential in the number of neurons. This paper combines two quantum computational algorithms to produce such a quantum associative memory. The result is an exponential increase in the capacity of the memory when compared to traditional associative memories such as the Hopfield network. The paper covers necessary high-level quantum mechanical and quantum computational ideas and introduces a quantum associative memory. Theoretical analysis proves the utility of the memory, and it is noted that a small version should be physically realizable in the near future.

Ventura, Dan and Tony Martinez, "Initializing the Amplitude Distribution of a Quantum State", submitted to Foundations of Physics Letters, June 1999.

Abstract:

To date, quantum computational algorithms have operated on a superposition of all basis states of a quantum system. Typically, this is because it is assumed that some function f is known and implementable as a unitary evolution. However, what if only some points of the function f are known? It then becomes important to be able to encode only the knowledge that we have about f. This paper presents an algorithm that requires a polynomial number of elementary operations for initializing a quantum system to represent only the m known points of a function f.

Refereed Conference Papers

Ventura, Dan, "Implementing Competitive Learning in a Quantum System", to appear in the Proceedings of the International Joint Conference on Neural Networks, July 1999.

Abstract:

Ideas from quantum computation are applied to the field of neural networks to produce competitive learning in a quantum system. The resulting quantum competitive learner has a prototype storage capacity that is exponentially greater than that of its classical counterpart. Further, empirical results from simulation of the quantum competitive learning system on real-world data sets demonstrate the quantum system's potential for excellent performance.
Also available in pdf.

Ventura, Dan, D. Randall Wilson, Brian Moncur and Tony Martinez, "A Neural Model of Centered Tri-gram Speech Recognition", to appear in the Proceedings of the International Joint Conference on Neural Networks, July 1999.

Abstract:

A relaxation network model that includes higher order weight connections is introduced. To demonstrate its utility, the model is applied to the speech recognition domain. Traditional speech recognition systems typically consider only that context preceding the word to be recognized. However, intuition suggests that considering following context as well as preceding context should improve recognition accuracy. The work described here tests this hypothesis by applying the higher order relaxation network to consider both precedes and follows context in a speech recognition task. The results demonstrate both the general utility of the higher order relaxation network as well as its improvement over traditional methods on a speech recognition task.
Also available in pdf.

Wilson, D. Randall, Dan Ventura, Brian Moncur and Tony Martinez "The Robustness of Relaxation Rates in Constraint Satisfaction Networks", to appear in the Proceedings of the International Joint Conference on Neural Networks, July 1999.

Abstract:

Constraint satisfaction networks contain nodes that receive weighted evidence from external sources and/or other nodes. A relaxation process allows the activation of nodes to affect neighboring nodes, which in turn can affect their neighbors, allowing information to travel through a network. When doing discrete udates (as in a software implementation of a relaxation network), a goal net or goal activation can be computed in response to the net input into a node, and a relaxation rate can then be used to determine how fast the node moves from its current value to its goal value. An open question was whether or not the relaxation rate is a sensitive parameter. This paper shows that the relaxation rate has almost no effect on how information flows through the network as long as it is small enough to avoid large descrete steps and/or oscillation.
This paper is currently only available in pdf.

Ventura, Dan and Tony Martinez, "A Quantum Associative Memory Based on Grover's Algorithm", to appear in the Proceedings of the International Conference on Artificial Neural Networks and Genetic Algorithms, April 1999.

Abstract:

Quantum computation uses microscopic quantum level effects to perform computational tasks and has produced results that in some cases are exponentially faster than their classical counterparts. The unique characteristics of quantum theory may also be used to create a quantum associative memory with a capacity exponential in the number of neurons. This paper combines two quantum computational algorithms to produce a quantum associative memory. The result is an exponential increase in the capacity of the memory when compared to traditional associative memories such as the Hopfield network. This paper covers necessary high- level quantum mechanical ideas and introduces a quantum associative memory, a small verson of which should be physically realizable in the near future.

Ventura, Dan, "Artificial Associative Memory using Quantum Processes", (invited paper) Proceedings of the International Conference on Computational Intelligence and Neuroscience, vol. 2, pp. 218-221, October 1998.

Abstract:

This paper discusses an approach to constructing an artificial quantum associative memory (QuAM). The QuAM makes use of two quantum computational algorithms, one for pattern storage and the other for pattern recall. The result is an exponential increase in the capacity of the memory when compared to traditional associative memories such as the Hopfield network. Further, the paper argues for considering pattern recall as a non-unitary process and demonstrates the utility of non-unitary operators for improving the pattern recall performance of the QuAM.

Ventura, Dan and Tony Martinez, "Quantum Associative Memory with Exponential Capacity", Proceedings of the International Joint Conference on Neural Networks, pp. 509-13, May 1998.

Abstract:

Quantum computation uses microscopic quantum level effects to perform computational tasks and has produced results that in some cases are exponentially faster than their classical counterparts by taking advantage of quantum parallelism. The unique characteristics of quantum theory may also be used to create a quantum associative memory with a capacity exponential in the number of neurons. This paper covers necessary high-level quantum mechanical ideas and introduces a simple quantum associative memory. Further, it provides discussion, empirical results and directions for future work.

Ventura, Dan and Tony Martinez, "Optimal Control Using a Neural/Evolutionary Hybrid System", Proceedings of the International Joint Conference on Neural Networks, pp. 1036-41, May 1998.

Abstract:

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 learns 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 the rapid execution and generalization of NN to produce a viable solution to the control problem. This paper presents the EC/NN hybrid and demonstrates its utility in developing a neurocontroller that demonstrates stability, generalization, and optimality.

Ventura, Dan, and Tony Martinez, "Using Evolutionary Computation to Facilitate Development of Neurocontrol", Proceedings of the International Workshop on Neural Networks and Neurocontrol, August 1997.

Abstract:

The field of neurocontrol, in which neural networks are used for control of complex systems, has many potential applications. 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). The survivors of this evolutionary process are used to construct a training set for the NN. The NN learns the training set, is able to generalize to new system states, and is then used for neurocontrol. Thus the EC/NN approach combines the broad, parallel search of EC with the rapid execution and generalization of NN to produce a viable solution to the control problem. This paper presents the EC/NN hybrid and demonstrates its utility in developing a neurocontroller for the pole balancing problem.

Ventura, Dan, and Tony Martinez, "An Artificial Neuron with Quantum Mechanical Properties", Proceedings of the International Conference on Artificial Neural Networks and Genetic Algorithms, pp. 482-485, April 1997.

Abstract:

Quantum computation uses microscopic quantum level effects to perform computational tasks and has produced results that in some cases are exponentially faster than their classical counterparts. Choosing the best weights for a neural network is a time consuming problem that makes the harnessing of this quantum parallelism appealing. This paper briefly covers high-level quantum theory and introduces a model for a quantum neuron.

Ventura, Dan, and Tony Martinez, "A General Evolutionary/Neural Hybrid Approach to Learning Optimization Problems", Proceedings of the World Congress on Neural Networks, pp. 1091-5, September 1996.

Abstract:

A method combining the parallel search capabilities of Evolutionary Computation (EC) with the generalization of Neural Networks (NN) for solving learning optimization problems is presented. Assuming a fitness function for potential solutions can be found, EC can be used to explore the solution space, and the survivors of the evolution can be used as a training set for the NN which then generalizes over the entire space. Because the training set is generated by EC using a fitness function, this hybrid approach allows explicit control of training set quality.

Ventura, Dan, and Tony Martinez, "Concerning a General Framework for the Development of Intelligent Systems", Proceedings of the IASTED International Conference on Artificial Intelligence, Expert Systems and Neural Networks, pp. 44-47, August 1996.

Abstract:

There exists on-going debate between Connectionism and Symbolism as to the nature of and approaches to cognition. Many viewpoints exist and various issues seen as important have been raised. This paper suggests that a combination of these methodologies will lead to a better overall model. The paper reviews and assimilates the opinions and viewpoints of these diverse fields and provides a cohesive list of issues thought to be critical to the modeling of intelligence. Further, this list results in a framework for the development of a general, unified theory of cognition.

Ventura, Dan, and Tony Martinez, "Robust Optimization Using Training Set Evolution", Proceedings of the International Conference on Neural Networks, pp. 524-8, 1996.

Abstract:

Training Set Evolution is an eclectic optimization technique that combines evolutionary computation (EC) with neural networks (NN). The synthesis of EC with NN provides both initial unsupervised random exploration of the solution space as well as supervised generalization on those initial solutions. An assimilation of a large amount of data obtained over many simulations provides encouraging empirical evidence for the robustness of Evolutionary Training Sets as an optimization technique for feedback and control problems.

Ventura, Dan and Tony Martinez, "An Empirical Comparison of Discretization Methods", Proceedings of the Tenth International Symposium on Computer and Information Sciences, pp. 443-450, 1995.

Abstract:

Many machine learning and neurally inspired algorithms are limited, at least in their pure form, to working with nominal data. However, for many real-world problems, some provision must be made to support processing of continuously valued data. This paper presents empirical results obtained by using six different discretization methods as preprocessors to three different supervised learners on several real-world problems. No discretization technique clearly outperforms the others. Also, discretization as a preprocessing step is in many cases found to be inferior to direct handling of continuously valued data. These results suggest that machine learning algorithms should be designed to directly handle continuously valued data rather than relying on preprocessing or ad hoc techniques.

Ventura, Dan and Tony Martinez, "Using Multiple Statistical Prototypes to Classify Continuously Valued Data", Proceedings of the International Symposium on Neuroinformatics and Neurocomputers, pp. 238-245, 1995.

Abstract:

Multiple Statistical Prototypes (MSP) is a modification of a standard minimum distance classification scheme that generates multiple prototypes per class using a modified greedy heuristic. Empirical comparison of MSP with other well-known learning algorithms shows MSP to be a robust algorithm that uses a very simple premise to produce good generalization and achieve parsimonious hypothesis representation.

Ventura, Dan, Tim Andersen and Tony Martinez, "Using Evolutionary Computation to Generate Training Set Data for Neural Networks", Proceedings of the International Conference on Artificial Neural Networks and Genetic Algorithms, pp. 468-471, 1995.

Abstract:

Most neural networks require a set of training examples in order to attempt to approximate a problem function. For many real-world problems, however, such a set of examples is unavailable. Such a problem involving feedback optimization of a computer network routing system has motivated a general method of generating artificial training sets using evolutionary computation. This paper describes the method and demonstrates its utility by presenting promising results from applying it to an artificial problem similar to a real-world network routing optimization problem.

Ventura, Dan and Tony Martinez, "BRACE: A Paradigm for the Discretization of Continuously Valued Data", Proceedings of the Tenth Florida Artificial Intelligence Research Symposium, pp. 117-121, 1994.

Abstract:

Discretization of continuously valued data is a useful and necessary tool because many learning paradigms assume nominal data. A list of objectives for efficient and effective discretization is presented. A paradigm called BRACE (Boundary Ranking And Classification Evaluation) that attempts to meet the objectives is presented along with an algorithm that follows the paradigm. The paradigm meets many of the objectives, with potential for extension to meet the remainder. Empirical results have been promising. For these reasons BRACE has potential as an effective and efficient method for discretization of continuously valued data. A further advantage of BRACE is that it is general enough to be extended to other types of clustering/unsupervised learning.

Other Refereed Publications

Ventura, Dan, Quantum and Evolutionary Approaches to Computational Learning, Ph.D. Dissertation, Computer Science Department, Brigham Young University, 1998.

Abstract:

This dissertation presents two methods for attacking the problem of high dimensional spaces inherent in most computational learning problems. The first approach is a hybrid system for combining the thorough search capabilities of evolutionary computation with the speed and generalization of neural computation. This neural/evolutionary hybrid is utilized in three different settings: to address the problem of data acquisition for training a supervised learning system; as a learning optimization system; and as a system for developing neurocontrol. The second approach is the idea of quantum computational learning that overcomes the "curse of dimensionality" by taking advantage of the massive state space of quantum systems to process information in a way that is classically impossible. The quantum computational learning approach results in the development of a neuron with quantum mechanical properties, a quantum associative memory and a quantum computational learning system for inductive learning.

Ventura, Dan, On Discretization as a Preprocessing Step for Supervised Learning Models, Master's Thesis, Computer Science Department, Brigham Young University, 1995.

Abstract:

Many machine learning and neurally inspired algorithms are limited, at least in their pure form, to working with nominal data. However, for many real- world problems, some provision must be made to support processing of continuously valued data. BRACE, a paradigm for the discretization of continuously valued attributes is introduced, and two algorithmic instantiations of this paradigm, VALLEY and SLICE are presented. These methods are compared empirically with other discretization techniques on several real-world problems and no algorithm clearly outperforms the others. Also, discretization as a preprocessing step is in many cases found to be inferior to direct handling of continuously valued data. These results suggest that machine learning algorithms should be designed to directly handle continuously valued data rather than relying on preprocessing or ad hoc techniques. To this end statistical prototypes (SP/MSP) are developed and an empirical comparison with well-known learning algorithms is presented. Encouraging results demonstrate that statistical prototypes have the potential to handle continuously valued data well. However, at this point, they are not suited for handling nominally valued data, which is arguably at least as important as continuously valued data in learning real-world applications. Several areas of ongoing research that aim to provide this ability are presented.