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1.5 Regression Networks for Meta-Learning Few-Shot Classification
Arnout Devos · Matthias Grossglauser

Sat Jul 18 07:10 AM -- 07:15 AM (PDT) @


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Author Information

Arnout Devos (Swiss Federal Institute of Technology Lausanne (EPFL))

Hi, I’m Arnout, a PhD student in Computer Science at the Swiss Federal Institute of Technology Lausanne (EPFL). In my research I focus on increasing the versatility of machine learning systems. I have mostly been working on meta-learning and few-shot learning algorithms to create models that can learn with only a handful of data. More recently, I have been working on unsupervised and self-supervised learning for few-shot settings, reducing the need for large labeled data even more. Previously, I did machine learning research at Stanford University, and obtained a Master’s in CS at USC and a Bachelor’s in EECS at KU Leuven.

Matthias Grossglauser (EPFL)

Matthias Grossglauser is Associate Professor in the School of Computer and Communication Sciences at EPFL. His current research interests are in machine learning for large social systems, stochastic models and algorithms for graph and mobility mining, and recommender systems. He is also the current director of the Doctoral School in Computer and Communication Sciences. From 2007-2010, he was with the Nokia Research Center (NRC) in Helsinki, Finland, serving as head of the Internet Laboratory, and later as head of a tech-transfer program focused on data mining, analytics, and machine learning. In addition, he served on Nokia's CEO Technology Council, a technology advisory group reporting to the CEO. Prior to this, he was Assistant Professor at EPFL, and a Research Scientist in the Networking and Distributed Systems Laboratory at AT&T Research in New Jersey. He received the 1998 Cor Baayen Award from the European Research Consortium for Informatics and Mathematics (ERCIM), the 2006 CoNEXT/SIGCOMM Rising Star Award, and two best paper awards. He served on the editorial board of IEEE/ACM Transactions on Networking, and on numerous Technical Program Committees.

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