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TBA
Max Welling Prof. Dr. Max Welling
University of Amsterdam, the Netherlands

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Biography:

Prof. Dr. Max Welling is a research chair in Machine Learning at the University of Amsterdam and a VP Technologies at Qualcomm. He has a secondary appointment as a senior fellow at the Canadian Institute for Advanced Research (CIFAR). He is co-founder of “Scyfer BV” a university spin-off in deep learning which got acquired by Qualcomm in summer 2017. In the past he held postdoctoral positions at Caltech (98-00), UCL (00-01) and the U. Toronto (01-03). He received his PhD in 98 under supervision of Nobel laureate Prof. G. 't Hooft. Max Welling has served as associate editor in chief of IEEE TPAMI from 2011-2015 (impact factor 4.8). He serves on the board of the NIPS foundation since 2015 (the largest conference in machine learning) and has been program chair and general chair of NIPS in 2013 and 2014 respectively. He was also program chair of AISTATS in 2009 and ECCV in 2016 and general chair of MIDL 2018. He has served on the editorial boards of JMLR and JML and was an associate editor for Neurocomputing, JCGS and TPAMI. He received multiple grants from Google, Facebook, Yahoo, NSF, NIH, NWO and ONR-MURI among which an NSF career grant in 2005. He is recipient of the ECCV Koenderink Prize in 2010. Welling is in the board of the Data Science Research Center in Amsterdam, he directs the Amsterdam Machine Learning Lab (AMLAB), and co-directs the Qualcomm-UvA deep learning lab (QUVA) and the Bosch-UvA Deep Learning lab (DELTA). Max Welling has over 200 scientific publications in machine learning, computer vision, statistics and physics.

Abstract:

TBA

Temporal models with low-rank spectrogram
Cedric Fevotte CNRS senior researcher Cédric Févotte
CNRS, Toulouse, France

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Cédric Févotte is a CNRS senior researcher at Institut de Recherche en Informatique de Toulouse (IRIT). Previously, he has been a CNRS researcher at Laboratoire Lagrange (Nice, 2013-2016) & Télécom ParisTech (2007-2013), a research engineer at Mist-Technologies (the startup that became Audionamix, 2006-2007) and a postdoc at University of Cambridge (2003-2006). He holds MEng and PhD degrees in EECS from École Centrale de Nantes. His research interests concern statistical signal processing and machine learning, in particular for source separation and inverse problems. He has been a member of the IEEE Machine Learning for Signal Processing technical committee (2012-2018) and a member of SPARS steering committee since 2018. He has been an associate editor for the IEEE Transactions on Signal Processing since 2014. In 2014, he was the co-recipient of an IEEE Signal Processing Society Best Paper Award for his work on audio source separation using multichannel nonnegative matrix factorisation. He is the principal investigator of the European Research Council (ERC) project FACTORY (New paradigms for latent factor estimation, 2016-2021).

Abstract:

TBA

End-to-end Speech Recognition Systems Explored
Dong Yu Distinguished Scientist and Vice General Manager, Dong Yu
Tencent AI Lab, Seattle, USA

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Biography:

Dr. Dong Yu is a distinguished scientist and vice general manager at Tencent AI Lab, an IEEE Fellow and an ACM Distinguished Scientist. Before joining Tencent in 2017, he was a principal researcher at Microsoft Research, where he joined in 1998. His research has been focusing on speech recognition and other applications of machine learning techniques. He has published two monographs and 160+ papers. His works have been cited over 17k times per Google Scholar and have been recognized by the prestigious IEEE Signal Processing Society 2013 and 2016 best paper award.

Dr. Dong Yu currently is serving as a member of the IEEE Speech and Language Processing Technical Committee (2013-2018) and a distinguished lecturer of APSIPA (2017-2018). He has served as an associate editor of the IEEE/ACM transactions on audio, speech, and language processing (2011-2015), an associate editor of the IEEE signal processing magazine (2008-2011), and members of organization and technical committees of many conferences and workshops.

Abstract:

In this talk, I will introduce and compare the most promising end-to-end speech recognition systems such as Connectionist Temporal Classification (CTC), RNN Transducer, RNN aligner, and sequence-to-sequence translation model with attention. I will discuss advantages and shortcomings of each setup, present key observations we have made when exploring these models, and discuss possible further developments.

Industrial Keynote: A reality check on data driven business – what are the real life potential and barriers?
Kaare Brandt Petersen Data Science Evangelist Dr. Kaare Brandt Petersen
Implement Consulting Group, Copenhagen, Denmark

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Kaare Brandt Petersen is an expert and enthusiast on data analysis and mathematical modeling for improvement of products, services, work processes, insight and life in general. He is also a passionate participant in discussions on how data analysis impacts the development of society, change the business landscape, and to what extend the human brain is suited for doing data analysis on its own. He is a driver for advance data analysis in Implement Consulting Group and is on a journey to find practical value of the modern advanced data analysis methods. Previously he has had positions such Director of Education & Academics in SAS, Business Manager and Advisor in SAS, Consultant and partner in Epital and as post doc at DTU. He holds a Ph.d. in mathematical modelling from Technical University of Denmark and has some scientific publications and The Matrix Cookbook on his academic resume. Today Kaare Brandt Petersen still has contact to the universities as censor at DTU, part of the program committee at Symposium i Anvendt Statistik and part of the Business employment panel at ITU.

Abstract:

When talking about data driven business we often look to the tech giants and seek inspiration and learning points about how to use data to be better at what we do. But a typical organization does not look like one of the tech giants at all and if such an organization want to start using data and has Google as point of reference, they are in for a rough ride. In this talk, I will present the key findings of interviewing people in many organizations about their use of advanced data analysis and their roadmap, expectations and aspirations about being data driven. Interestingly there are some notable differences from the classical high-profile statements and we also look into the human factor of the data driven future.
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