2015 DeepLearningArchitecturewithDyn

• (Veeriah et al., 2015) ⇒ Vivek Veeriah, Rohit Durvasula, and Guo-Jun Qi. (2015). “Deep Learning Architecture with Dynamically Programmed Layers for Brain Connectome Prediction.” In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2015). ISBN:978-1-4503-3664-2 doi:10.1145/2783258.2783399

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222015%22+Deep+Learning+Architecture+with+Dynamically+Programmed+Layers+for+Brain+Connectome+Prediction
• http://dl.acm.org/citation.cfm?id=2783258.2783399&preflayout=flat#citedby

Quotes

Author Keywords

• Brain connectome prediction; deep learning; dynamically programmed layer; neural nets; time-series alignment

Abstract

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 architecture that exploits the spatial and temporal nature of the neuronal activation data. The architecture consists of a combination of Convolutional layer and a Recurrent layer for predicting the connectome of neurons based on their time-series of activation data. The key contribution of this paper is a dynamically programmed layer that is critical in determining the alignment between the neuronal activations of pair-wise combinations of neurons.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2015 DeepLearningArchitecturewithDyn	Guo-Jun Qi Vivek Veeriah Rohit Durvasula			Deep Learning Architecture with Dynamically Programmed Layers for Brain Connectome Prediction				10.1145/2783258.2783399		2015