CompSci Colloquium: David Inouye on "Deeper Understanding of Deep Learning"

Deeper Understanding of Deep Learning via Shallow Learning and Model Explanations

ABSTRACT: Despite tremendous empirical success, modern deep learning is still relatively new, and thus, there are significant gaps in understanding compared to classical shallow learning. This lack of deep understanding of deep learning hinders practitioners from systematically and reliably developing models especially in new contexts—thus development is often relegated to laborious trial and error. In addition, this lack of understanding hinders users from adopting deep models in real-world applications. As one approach for deeper understanding, I will discuss how to leverage well-understood shallow learning to construct deep models so that the algorithms and insights from shallow learning can be lifted into the deep context. Specifically, I will present a destructive process that iteratively finds patterns in the data via shallow learning and then destroys these patterns via transformations. I will then show an application of this unconventional deep learning approach to deep probabilistic models. As a different approach, model explanations can increase users' understanding of deep models and thereby aid them in deciding if they should adopt a deep model or not. Thus, I will also describe how to create model explanations based on the concept of counterfactuals that are simultaneously exact and non-local—in contrast to explanations based on local approximations of the model. I will present a new framework for finding useful model explanations, conceptualized as lines or curves in the input space that compress the full model into a few relevant trajectories for a given target point, where relevancy depends on the context. In conclusion, I will discuss promising future directions for both destructive learning and model explanation.

BIO: David Inouye is a postdoctoral researcher at Carnegie Mellon University in the Machine Learning Department working with Prof. Pradeep Ravikumar. He completed his PhD in CS at The University of Texas at Austin where he was advised by Prof. Inderjit Dhillon and Prof. Pradeep Ravikumar. David's research interests include deep generative models, probabilistic graphical models, model explanations and model visualizations. He completed his BS in EE at Georgia Institute of Technology and was awarded the NSF GRFP graduate research fellowship during his senior year.
 

Monday, March 4, 2019 at 3:30pm to 4:30pm

Discovery Learning Center, DLC 170
1095 Regent Drive, Boulder, CO 80309

Event Type

Colloquium/Seminar

College, School & Unit

Engineering & Applied Science

Group
Computer Science
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