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Prof. H. Kjellstrom: Learning Factorized Latent Representations Using IBTM

Prof. H. Kjellstrom, KTH Stockholm
Data dell'evento: 
Thursday, 29 September, 2016 - 11:00
Room B 101
B. Caputo

In this talk I will introduce the Inter-Battery Topic Model (IBTM). Our approach extends traditional topic models by learning a factorized latent variable representation. The structured representation leads to a model that marries benefits traditionally associated with a discriminative approach, such as feature selection, with those of a generative model, such as principled regularization and ability to handle missing data. The factorization is provided by representing data in terms of aligned pairs of observations as different views. This  provides means for selecting a representation that separately models topics that exist in both views from the topics that are unique to a single view. This structured consolidation allows for efficient and robust inference and provides a compact and efficient representation.

Hedvig Kjellström is a Professor of Computer Science and the head of the Computer Vision and Active Perception Lab (CVAP) at KTH in Stockholm, Sweden. She received an MSc in Engineering Physics and a PhD in Computer Science from KTH in 1997 and 2001, respectively. The topic of her doctoral thesis was 3D reconstruction of human motion in video. Between 2002 and 2006 she worked as a scientist at the Swedish Defence Research Agency, where she focused on Information Fusion and Sensor Fusion. In 2007 she returned to KTH, pursuing research in activity analysis in video. Her present research focuses on the modeling of perception and production of human non-verbal communicative behavior and activity, with applications in Health, Robotics, and Performing Arts.
In 2010, she was awarded the Koenderink Prize for fundamental contributions in Computer Vision for her ECCV 2000 article on human motion reconstruction, written together with Michael Black and David Fleet. She has written around 70 papers in the fields of Robotics, Computer Vision, Information Fusion, Machine Learning, Cognitive Science, Speech, and Human-Computer Interaction. She is mostly active within the areas of Robotics and Computer Vision, where she is an Associate Editor for IEEE TPAMI and IEEE RA-L, and an Area Chair for CVPR 2016 and RSS 2016.

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