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Photo of Tamir Hazan
Assistant Professor
Perturbation models

Statistically reasoning about complex systems involves a probability distribution over exponentially many configurations. For example, semantic labeling of an image requires to infer a discrete label for each image pixel, hence resulting in possible segmentations which are exponential in the numbers of pixels. Standard approaches such as Gibbs sampling are slow in practice and cannot be applied to many real-life problems. Our goal is to integrate optimization and sampling through extreme value statistics and to define new statistical framework for which sampling and parameter estimation in complex systems are efficient. This framework is based on measuring the stability of prediction to random changes in the potential interactions.

Factor Graph Attention models

Deep learning revolutionized AI and machine learning techniques can be used to achieve human-like behavior. To better address complex tasks such as visual-dialog or visual navigation we designed a general attention mechanism that use a factor graph based attention mechanism which can combines high-dimensional information that govern complex tasks. This framework allowed us to win the visual dialog challenge of CVPR 2020