Understanding Deep Learning Models with Visual Analytics (ML+VA) 

Machine learning, especially deep learning with neural networks, has achieved unprecedented success in a variety of disciplines, such as object recognition with convolutional neural networks (CNN), speech recognition with recurrent neural networks (RNN), and image generation with generative adversarial networks (GAN). However, to date, there is no clear understanding on why these complicated neural networks perform so well, and how they might be improved. In GRAVITY lab, we resort to visual analytics approaches to fill the gap between the success of deep learning models and the deficiency in model interpretations. In collaboration with domain scientists, we develop integrated visual analytics systems to demonstrate model details, explore training dynamics in different levels with friendly user interactions, and propose potential solutions to improve the performance of machine learning models. 

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