Time: Tuesday, March 3, 2018, 2:00pm @ MTH3207
Speaker: Jeremiah (Jerry) Emidih (UMD)
Title: Deep Geometric Learning and Data Fusion spaces
Abstract: This talk will survey recent developments in machine learning with non-Euclidean data. There are many settings in which both standard Fourier techniques and modern neural network schemes fail to capture intrinsic data features. In these cases, data-dependent representations must be learned from geometric properties of the data. We will consider a few graph-based data representations and examine models for extending convolutional neural networks to geometries without the highly regularized structure of Euclidean space. We will also investigate some applications of geometric learning to genomics and neuroscience.
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