Visualizing neural network activity

In this session, Justin is going to examine feature salience, and try to open the black box of deep learning algorithms. For example, visual features identified in image classification tasks are inherently suitable for representing the decision boundary of a classifier. But what to do when the features are audio-based? Or syntactic? Join us for an overview of current innovative efforts to expose the selection process of deep learners and a lively discussion on how to communicate the inner workings of your favorite deep learning algorithm.

About the speaker:

Justin is a research assistant in the Institute of Mathematics at the University of Osnabrück, Germany. He is also an Intel software innovator from the US and studies cognitive science in Germany with a focus on machine learning and computer vision. Before studying cognitive science, he worked in neuroscience research with neuronal and brain activation imaging. He founded and organized the San Antonio Science Café and the Tel Aviv Science Café, and is preparing his master's thesis on visualizing deep learning neural networks.