Graphs and more Complex structures for Learning and Reasoning (GCLR) workshop was held at AAAI 2021. For more details about the workshop, please visit website: https://sites.google.com/view/gclr2021/.
Speaker's Bio: Prof. Anima Anandkumar is a Bren Professor at Caltech and Director of ML Research at NVIDIA. She was previously a Principal Scientist at Amazon Web Services. She has received several honors such as Alfred. P. Sloan Fellowship, NSF Career Award, Young investigator awards from DoD, and Faculty Fellowships from Microsoft, Google, Facebook, and Adobe. She is part of the World Economic Forum's Expert Network. She is passionate about designing principled AI algorithms and applying them in interdisciplinary applications. Her research focus is on unsupervised AI, optimization, and tensor methods.
Title of the talk: Infusing Structure and Domain Knowledge into Deep Learning
Abstract: The deep-learning revolution has achieved impressive progress through the convergence of data, algorithms, and computing infrastructure. However, for further progress, we cannot solely rely on bigger models. We need to reduce our dependence on labeled data, and design algorithms that can incorporate more structure and domain knowledge. Examples include tensors, graphs, physical laws, and simulations. I will describe efficient frameworks that enable developers to easily prototype such models, e.g. Tensorly-torch to incorporate tensorized architectures. Compositionality is an important hallmark of intelligence. Humans are able to compose concepts to reason about entirely new scenarios. We have created a new dataset for few-shot learning, inspired by the Bongard challenge. We show that all existing meta learning methods severely fall short of human performance. We demonstrate that neuro-symbolic reasoning is critical for tackling such few-shot learning challenges.