Deep networks have had profound impact across machine learning research and in many application areas. DNNs are complex to design and train. They are non-linear systems that almost always have many local optima and are often sensitive to training parameter settings and initial state. Systematic optimization of structure and hyperparameters is possible e.g. with Bayesian optimization, but hampered by the expense of training each design on realistic datasets. Exploration is still ongoing for best design principles. We argue that visualization can play an essential role in understanding DNNs and in developing new design principles. With rich tools for visual exploration of networks during training and inference, one should be able to form closer ties between theory and practice: validating expected behaviors, and exposing the unexpected which can lead to new insights. With the rise of generative modeling and reinforcement learning, more interesting directions like understanding and visualization of generative models, visual explanation for driving policy could be explored as well.

As the second edition of this workshop, we are proposing changes based on the lessons we learned last year. We would like to organize a few domain specific tutorials, and panel discussions. We do think machine learning researchers need a lot of tutorials and advice from the visualization/HCI community and vice versa. Many audience in our workshop last year also suggested that more discussion can greatly help us better define such interdisciplinary area.