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Efficient labeling technique and interpretable deep neural network for the classification of seizures using continuous electroencephalograms
(Georgia Institute of Technology, 2018-04-24)
This thesis focuses on the classification of seizures, together with finding efficient and scalable ways to obtain high-quality datasets in order to train deep neural networks. It was motivated by the need to automate the ...
Teaching robots about human environments: Leveraging human interaction to efficiently learn and use multisensory object affordances
(Georgia Institute of Technology, 2018-01-09)
The real world is complex, unstructured, and contains high levels of uncertainty. Although past work shows that robots can successfully operate in situations where a single skill is needed, they will need a framework that ...
Enabling one-handed input for wearable computing
(Georgia Institute of Technology, 2017-05-30)
A new evolution of computing is emerging around wearable technologies. Wearable computing has been a topic of research for years. However, we are beginning to see adoption by consumers and non-researchers due to advances ...
A computational model for solving raven’s progressive matrices intelligence test
(Georgia Institute of Technology, 2018-05-11)
Graphical models offer techniques for capturing the structure of many problems in real- world domains and provide means for representation, interpretation, and inference. The modeling framework provides tools for discovering ...
Novel document representations based on labels and sequential information
(Georgia Institute of Technology, 2015-07-23)
A wide variety of text analysis applications are based on statistical machine learning techniques. The success of those applications is critically affected by how we represent a document. Learning an efficient document ...
Neuro-general computing an acceleration-approximation approach
(Georgia Institute of Technology, 2018-07-30)
A growing number of commercial and enterprise systems rely on compute and power intensive tasks. While the demand of these tasks is growing, the performance benefits from general-purpose platforms are diminishing. Without ...
Manipulating state space distributions for sample-efficient imitation-learning
(Georgia Institute of Technology, 2020-03-16)
Imitation learning has emerged as one of the most effective approaches to train agents to act intelligently in unstructured and unknown domains. On its own or in combination with reinforcement learning, it enables agents ...
Disease progression modeling using multi-dimensional continuous-time hidden Markov model
(Georgia Institute of Technology, 2015-08-25)
Continuous-Time Hidden Markov Model (CT-HMM) is a useful tool for modeling disease progression from noisy observed data arriving irregularly in time. However, the lack of any widely-accepted efficient learning algorithm ...
Large scale machine learning for geospatial problems in computational sustainability
(Georgia Institute of Technology, 2020-05-14)
The UN laid out 17 Sustainable Development Goals as part of the “The 2030 Agenda for Sustainable Development”. Each goal consists of broad targets - such as increasing the percentage of forested land (indicator 15.1.1) - ...
Integrating reinforcement learning into a programming language
(Georgia Institute of Technology, 2017-06-26)
Reinforcement learning is a promising solution to the intelligent agent problem, namely, given the state of the world, which action should an agent take to maximize goal attainment. However, reinforcement learning algorithms ...