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Artificial Intelligence and Robotics
University of Arkansas, Fayetteville
Computer Science and Computer Engineering Undergraduate Honors Theses
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Full-Text Articles in Theory and Algorithms
Gauging The State-Of-The-Art For Foresight Weight Pruning On Neural Networks, Noah James
Gauging The State-Of-The-Art For Foresight Weight Pruning On Neural Networks, Noah James
Computer Science and Computer Engineering Undergraduate Honors Theses
The state-of-the-art for pruning neural networks is ambiguous due to poor experimental practices in the field. Newly developed approaches rarely compare to each other, and when they do, their comparisons are lackluster or contain errors. In the interest of stabilizing the field of pruning, this paper initiates a dive into reproducing prominent pruning algorithms across several architectures and datasets. As a first step towards this goal, this paper shows results for foresight weight pruning across 6 baseline pruning strategies, 5 modern pruning strategies, random pruning, and one legacy method (Optimal Brain Damage). All strategies are evaluated on 3 different architectures …
Semi-Supervised Spatial-Temporal Feature Learning On Anomaly-Based Network Intrusion Detection, Huy Mai
Semi-Supervised Spatial-Temporal Feature Learning On Anomaly-Based Network Intrusion Detection, Huy Mai
Computer Science and Computer Engineering Undergraduate Honors Theses
Due to a rapid increase in network traffic, it is growing more imperative to have systems that detect attacks that are both known and unknown to networks. Anomaly-based detection methods utilize deep learning techniques, including semi-supervised learning, in order to effectively detect these attacks. Semi-supervision is advantageous as it doesn't fully depend on the labelling of network traffic data points, which may be a daunting task especially considering the amount of traffic data collected. Even though deep learning models such as the convolutional neural network have been integrated into a number of proposed network intrusion detection systems in recent years, …
Applying Imitation And Reinforcement Learning To Sparse Reward Environments, Haven Brown
Applying Imitation And Reinforcement Learning To Sparse Reward Environments, Haven Brown
Computer Science and Computer Engineering Undergraduate Honors Theses
The focus of this project was to shorten the time it takes to train reinforcement learning agents to perform better than humans in a sparse reward environment. Finding a general purpose solution to this problem is essential to creating agents in the future capable of managing large systems or performing a series of tasks before receiving feedback. The goal of this project was to create a transition function between an imitation learning algorithm (also referred to as a behavioral cloning algorithm) and a reinforcement learning algorithm. The goal of this approach was to allow an agent to first learn to …
Music Feature Matching Using Computer Vision Algorithms, Mason Hollis
Music Feature Matching Using Computer Vision Algorithms, Mason Hollis
Computer Science and Computer Engineering Undergraduate Honors Theses
This paper seeks to establish the validity and potential benefits of using existing computer vision techniques on audio samples rather than traditional images in order to consistently and accurately identify a song of origin from a short audio clip of potentially noisy sound. To do this, the audio sample is first converted to a spectrogram image, which is used to generate SURF features. These features are compared against a database of features, which have been previously generated in a similar fashion, in order to find the best match. This algorithm has been implemented in a system that can run as …
Ant Colony Optimization For Continuous Spaces, Rachel Findley
Ant Colony Optimization For Continuous Spaces, Rachel Findley
Computer Science and Computer Engineering Undergraduate Honors Theses
Ant Colony Optimization (ACO) is an optimization algorithm designed to find semi-optimal solutions to Combinatorial Optimization Problems. The challenge of modifying this algorithm to effectively optimize over a continuous domain is one that has been tackled by several researchers. In this paper, ACO has been modified to use several variations of the algorithm for continuous spaces. An aspect of ACO which is crucial to its success when optimizing over a continuous space is choosing the appropriate object (solution component) out of an infinite set to add to the ant's path. This step is highly important in shaping good solutions. Important …