GAMES Webinar 2020 – 145期(可视化专题) | Bei Wang(University of Utah)

GAMES Webinar 2020-145期】(可视化专题)

报告嘉宾:Bei Wang(University of Utah)


报告题目:TopoAct: Visually Exploring the Shape of Activations in Deep Learning


Deep neural networks such as GoogLeNet and ResNet have achieved impressive performance in tasks like image classification. To understand how such performance is achieved, we can probe a trained deep neural network by studying neuron activations, that is, combinations of neuron firings, at any layer of the network in response to a particular input. With a large set of input images, we aim to obtain a global view of what neurons detect by studying their activations. We ask the following questions: What is the shape of the space of activations? That is, what is the organizational principle behind neuron activations, and how are the activations related within a layer and across layers? Applying tools from topological data analysis, we present TopoAct, a visual exploration system used to study topological summaries of activation vectors for a single layer as well as the evolution of such summaries across multiple layers. We present visual exploration scenarios using TopoAct that provide valuable insights towards learned representations of an image classifier. This is joint work with Archit Rathore, Nithin Chalapathi, and Sourabh Palande.


Bei Wang is currently an Assistant Professor in the School of Computing and a faculty member in the Scientific Computing and Imaging(SCI) Institute. Her research expertise lies in the theoretical, algorithmic, and application aspects of data analysis and data visualization, with a focus on topological techniques. In particular, her research leverages topological data analysis, which provides a a strong theoretical basis for transforming large, complex data into compact, structure-highlighting representations. Such representations connect naturally with and provide infrastructures for data visualization, and inspire the rethinking of interactive data exploration to facilitate analytical reasoning. Some of her current research activities draw inspirations from topology, geometry, and machine learning, in studying vector fields, tensor fields, high-dimensional point clouds, networks, and multivariate ensembles.




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