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Skip to Search Results- 144Machine Learning
- 19Artificial Intelligence
- 17Reinforcement Learning
- 15Deep Learning
- 10Computer Vision
- 10Natural Language Processing
- 2Wen, Junfeng
- 1Aghaei, Nikoo
- 1Al Dallal, Ahmed
- 1Al-Masri, Mohammad
- 1Alam Anik, Md Tanvir
- 1Alateeq, Majed Mohammad
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Fall 2019
Artificial agents have been shown to learn to communicate when needed to complete a cooperative task. Some level of language structure (e.g., compositionality) has been found in the learned communication protocols. This observed structure is often the result of specific environmental pressures...
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Enhancing the Architecture of Context-Aware Driver Assistance Systems by Incorporating Insights from Naturalistic Driving Data
DownloadSpring 2019
Driving assistance systems (DASs) have received a great deal of attention in the past decades as an active and effective collision countermeasure. DASs potential benefits will be attained by enhancing the systems’ awareness regarding the dynamic driving context including the change in the driver...
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Estimating the Overlap of Top Instances in Lists Ranked by Correlation to Label
Spring 2012
Recent advances in high-throughput technologies, such as genome-wide SNP analysis and microar- ray gene expression profiling, have led to a multitude of ranked lists, where the features (SNPs, genes) are sorted based on their individual correlation with a phenotype. Multiple reviews have shown...
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Exploring biomarkers to predict pig disease resilience traits under a natural disease challenge
DownloadSpring 2024
The intensification and consolidation of modern pig production is exposed to higher risks of endemic or pandemic infections. The complexity of the polymicrobial challenge and increasing concerns on antibiotics resistance make it pivotal to find an efficient way of controlling infections besides...
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Fall 2022
Overfitting is a phenomenon when a machine learning system learns the patterns in training data so well that it starts to inauspiciously affect the model performance on unseen data. In practice, machine learning systems that overfit are not deployable rather systems that generalize well and do...
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Spring 2023
As we listen to spoken language, the brain performs multiple levels of computation, from understanding individual words to comprehending the arc of a story. Recently, computational models have been developed that also process text on multiple levels. These models, called multi-timescale long...
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Fall 2016
In this thesis, we investigate the move prediction problem in the game of Go by proposing a new ranking model named Factorization Bradley Terry (FBT) model. This new model considers the move prediction problem as group competitions while also taking the interaction between features into account....
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Fall 2019
Data is becoming more valuable as there are still many uncertainties and hidden information that have yet to be discovered. For this reason, the application of data analysis and machine learning in the industry is becoming more popular. For example, SAGD (steam assisted gravity drainage) is a...
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Fall 2021
Convolutional Neural Networks (CNNs) have been recently seeing great success in various image classification fields and applications. However, this success has been accompanied by a significant increase in memory and computational demands, limiting their use in resource-limited devices, e.g.,...
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Fixed Point Propagation: A New Way To Train Recurrent Neural Networks Using Auxiliary Variables
DownloadFall 2019
Recurrent neural networks (RNNs), along with their many variants, provide a powerful tool for online prediction in partially observable problems. Two issues concerning RNNs, however, are the ability to capture long-term dependencies and long training times. There have been a variety of strategies...