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The long short-term memory (LSTM) neural network is capable of processing complex sequential information since it utilizes special gating schemes for learning representations from long input sequences. It has the potential to model any time-series or sequential data, where the current hidden state has to be considered in the context of the past hidden(More)
This paper explores the idea of using deep neural network architecture with dynamically programmed layers for brain connectome prediction problem. Understanding the brain connectome structure is a very interesting and a challenging problem. It is critical in the research for epilepsy and other neuropathological diseases. We introduce a new deep learning(More)
An important application of interactive machine learning is extending or amplifying the cognitive and physical capabilities of a human. To accomplish this, machines need to learn about their human users' intentions and adapt to their preferences. In most current research, a user has conveyed preferences to a machine using explicit corrective or instructive(More)
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