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Full-Text Articles in Computer Engineering
Flight Simulator Modeling Using Recurrent Neural Networks, Nickolas Sabatini, Andreas Natsis
Flight Simulator Modeling Using Recurrent Neural Networks, Nickolas Sabatini, Andreas Natsis
Undergraduate Research & Mentoring Program
Recurrent neural networks (RNNs) are a form of machine learning used to predict future values. This project uses RNNs tor predict future values for a flight simulator. Coded in Python using the Keras library, the model demonstrates training loss and validation loss, referring to the error when training the model.
The Applications Of Grid Cells In Computer Vision, Keaton Kraiger
The Applications Of Grid Cells In Computer Vision, Keaton Kraiger
Undergraduate Research & Mentoring Program
In this study we present a novel method for position and scale invariant object representation based on a biologically-inspired framework. Grid cells are neurons in the entorhinal cortex whose multiple firing locations form a periodic triangular array, tiling the surface of an animal’s environment. We propose a model for simple object representation that maintains position and scale invariance, in which grid maps capture the fundamental structure and features of an object. The model provides a mechanism for identifying feature locations in a Cartesian plane and vectors between object features encoded by grid cells. It is shown that key object features …
Exploring And Expanding The One-Pixel Attack, Umairullah Khan, Walt Woods
Exploring And Expanding The One-Pixel Attack, Umairullah Khan, Walt Woods
Undergraduate Research & Mentoring Program
In machine learning research, adversarial examples are normal inputs to a classifier that have been specifically perturbed to cause the model to misclassify the input. These perturbations rarely affect the human readability of an input, even though the model’s output is drastically different. Recent work has demonstrated that image-classifying deep neural networks (DNNs) can be reliably fooled with the modification of a single pixel in the input image, without knowledge of a DNN’s internal parameters. This “one-pixel attack” utilizes an iterative evolutionary optimizer known as differential evolution (DE) to find the most effective pixel to perturb, via the evaluation of …
Combining Algorithms For More General Ai, Mark Robert Musil
Combining Algorithms For More General Ai, Mark Robert Musil
Undergraduate Research & Mentoring Program
Two decades since the first convolutional neural network was introduced the AI sub-domains of classification, regression and prediction still rely heavily on a few ML architectures despite their flaws of being hungry for data, time, and high-end hardware while still lacking generality. In order to achieve more general intelligence that can perform one-shot learning, create internal representations, and recognize subtle patterns it is necessary to look for new ML system frameworks. Research on the interface between neuroscience and computational statistics/machine learning has suggested that combined algorithms may increase AI robustness in the same way that separate brain regions specialize. In …