Latent Data Augmentation and Modular Structure for Improved Generalization

Speaker:        Alex LAMB
                University of Montreal

Title:          "Latent Data Augmentation and Modular Structure for
                 Improved Generalization"

Date:           Monday, 25 January 2021

Time:           10:00am - 11:00am

Zoom meeting:
https://hkust.zoom.us/j/465698645?pwd=c2E4VTE3b2lEYnBXcyt4VXJITXRIdz09

Meeting ID:     465 698 645
Passcode:       20202021


Abstract:

Deep neural networks have seen dramatic improvements in performance, with
much of this improvement being driven by new architectures and training
algorithms with better inductive biases. At the same time, the future of
AI is systems which run in an open-ended way which run on data unlike what
was seen during training and which can be drawn from a changing or
adversarial distribution. These problems also require a greater scale and
time horizon for reasoning as well as consideration of a complex world
system with many reused structures and subsystems. This talk will survey
some areas where deep networks can improve their biases as well as my
research in this direction. These algorithms dramatically change the
behavior of deep networks, yet they are highly practical and easy to use,
conforming to simple interfaces that allow them to easily be dropped into
existing codebases.


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Biography:

Introduction: I am currently a PhD student at the University of Montreal
advised by Yoshua Bengio and a recipient of the Twitch PhD Fellowship
2020. My research is on the intersection of developing new algorithms for
machine learning and new applications. In the area of algorithms, I'm
particularly interested in (1) making deep networks more modular and
richly structured and (2) improving the generalization performance of deep
networks, especially across shifting domains. I am particularly interested
in techniques which use functional inspiration from the brain and
psychology to improve performance on real tasks. In terms of applications
of Machine Learning, my most recent work has been on historical Japanese
documents and has resulted in KuroNet, a publicly released service which
makes classical Japanese documents (more) understandable to readers of
modern Japanese.