Deep Learning Theory Team Seminar (Mr. Alex LAMB)

This is an online seminar. Registration is required.
We’ll send the instruction for attending the online seminar.

【Deep Learning Theory Team】
【Date】2021/March/22 (Mon)

【Speaker】 Mr. Alex LAMB

Title: Latent Data Augmentation and Modular Structure for Improved Generalization

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.

https://u-tokyo-ac-jp.zoom.us/j/96489539583?pwd=VzIrMVF5M0ZoWGVUV25SYzhpZjM2UT09
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