Object-Oriented Learning (OOL): Perception, Representation, and Reasoning

International Conference on Machine Learning (ICML)

Friday July 17, 2020, Virtual Workshop

Access the virtual workshop page

Fabien Baradel has just completed his PhD at INSA Lyon under direction of Christian Wolf and Julien Mille. During his PhD is has working on structured models for video analysis with a focus on task such as action recognition and counterfactual prediction. In Fall, he will be joining Naver Labs Europe as a research scientist for working on similar topics with a long term research interest to develop efficient and reliable models for video understanding in the wild.
Jody Culham is a Professor in the Department of Psychology at Western University in London, Ontario. Her research focuses on how vision is used for perception and to guide actions in human observers. In order to answer these questions, she makes use of several techniques from cognitive neuroscience, including functional Magnetic Resonance Imaging (fMRI) and behavioral testing.
Moira Dillon is an Assistant Professor of Psychology at New York University and directs the Lab for the Developing Mind. Her work uses cognitive, developmental, and computational approaches to gain insight into the origin of abstract thought. A central thrust of her work concerns the development of human geometry, from the basic spatial sensitivities of infants, to the untutored use of spatial symbols and language by children, to the high-level spatial concepts of adults. Her work also explores how basic mechanisms of perception and cognition about objects, agents, and places might shape the products of human culture. Dillon is supported by an NSF CAREER Award, a Jacobs Foundation Early Career Fellowship, and DARPA.
Klaus Greff is a Research Scientist at Google Brain in Berlin and a PhD student at IDSIA with Jürgen Schmidhuber. His research focuses on the binding problem in neural networks, on learning object representations, and in particular on unsupervised object perception. His work received an outstanding paper award from IEEE Transactions on Neural Networks and Learning Systems.
Thomas Kipf is a Research Scientist at Google Research in the Brain Team in Amsterdam. He has recently completed his PhD at University of Amsterdam under Prof. Max Welling on the topic "Deep Learning with Graph-Structured Representations". His research focuses on graph representation learning and relational structure discovery with applications to network analysis, modeling of physical systems, object-centric learning and reasoning, and model-based learning in agents. He has co-organized a series of graph representation learning workshops at ICML, ICLR, NeurIPS, KDD, and ELLIS.
Igor Mordatch is a Senior Research Scientist at Google Brain. He obtained his PhD in Computer Science from the University of Washington in 2016 with Emo Todorov and Zoran Popovic, and subsequently did a postdoc with Pieter Abbeel at UC Berkeley. His research interests include model-based control, robotics, and multi-agent reinforcement learning. His work received best paper award at ICLR, and press coverage from Wired, MIT Technology Review, and others.
Vincent Sitzmann just finished his PhD at Stanford University with a thesis on "Self-Supervised Scene Representation Learning". His research interest lies in neural scene representations - the way neural networks learn to represent information on our world. His goal is to allow independent agents to reason about our world given visual observations, such as inferring a complete model of a scene with information on geometry, material, lighting etc. from only few observations, a task that is simple for humans, but currently impossible for AI. In July, Vincent will join Joshua Tenenbaum's group at MIT CSAIL for a Postdoc.
Linda Smith, Distinguished Professor at Indiana University Bloomington, is an internationally recognized leader in cognitive science and cognitive development. Taking a complex systems perspective, she seeks to understand the interdependencies among perceptual, motor and cognitive developments during the first three years of post-natal life. Using wearable sensors, including head-mounted cameras, she studies how the young learner’s own behavior creates learning experiences. The work has led to novel insights currently being extended through collaborations to robotics and artificial intelligence. She received her PhD from the University of Pennsylvania in 1977 and immediately joined the faculty at Indiana University. She won the David E. Rumelhart Prize for theoretical contributions to cognitive science and is an elected member of both the National Academy of Sciences and the American Academy of Arts and Science.