Exploiting symmetry in structured data is a powerful way to improve learning and generalization ability of AI systems, and extract more information, in applications from vision and NLP to robotics. This is exemplified by convolutional neural nets, which are an ubiquitous architecture. Recently, there has been a great deal of progress to develop improved equivariant and invariant learning architectures, as well as improved data augmentation methods. There has also been progress on the theoretical foundations of the area, from the perspectives of statistics and optimization. The notion of adding data via data augmentation also arises in problems such as adversarial robustness. This workshop brought together leading researchers in the area to discuss the state of the art of the field. The activity is part of the Center for Foundations of Information Processing at Penn, supported by NSF TRIPODS.
Due to Covid-19, the workshop was held online as a Zoom webinar. While this means less in-person interaction, it also means that the talks are accessible to everyone for free, without the costs of travel.
Recorded talks are available as a Youtube playlist. Individual videos are also linked below. The link below takes you to the playlist in the “play all” mode.