Understanding Neural Networks via Feature Visualization: A survey

    Abstract

    A neuroscience method to understanding the brain is to find and study the preferred stimuli that highly activate an individual cell or groups of cells. Recent advances in machine learning enable a family of methods to synthesize preferred stimuli that cause a neuron in an artificial or biological brain to fire strongly. Those methods are known as Activation Maximization (AM) or Feature Visualization via Optimization. In this chapter, we (1) review existing AM techniques in the literature; (2) discuss a probabilistic interpretation for AM; and (3) review the applications of AM in debugging and explaining networks.

    Authors

    Anh Nguyen, Jason Yosinski, Jeff Clune

    Publication

    Interpretable AI: Interpreting, Explaining and Visualizing Deep Learning

    Full Paper

    ‘Understanding Neural Networks via Feature Visualization: A survey’ (PDF)

    Uber AI

    Comments
    Previous articleExact Gaussian Processes on a Million Data Points
    Next articleMetropolis-Hastings Generative Adversarial Networks
    Jason Yosinski
    Jason Yosinski is a machine learning researcher and founding member of Uber AI Labs, where he uses neural networks to build more capable and more understandable AI.
    Jeff Clune
    Jeff Clune is the Loy and Edith Harris Associate Professor in Computer Science at the University of Wyoming and a Senior Research Manager and founding member of Uber AI Labs, which was formed after Uber acquired the startup Geometric Intelligence. Jeff focuses on robotics and training neural networks via deep learning and deep reinforcement learning. He has also researched open questions in evolutionary biology using computational models of evolution, including studying the evolutionary origins of modularity, hierarchy, and evolvability. Prior to becoming a professor, he was a Research Scientist at Cornell University, received a PhD in computer science and an MA in philosophy from Michigan State University, and received a BA in philosophy from the University of Michigan. More about Jeff’s research can be found at JeffClune.com