Real world data in many domains is multimodal and heterogeneous, such as healthcare, social media, and climate science. Tensors, as generalizations of vectors and matrices, provide a natural and scalable framework for handling data with inherent structures and complex dependencies. Recent renaissance of tensor methods in machine learning ranges from academic research on scalable algorithms for tensor operations, novel models through tensor representations, to industry solutions including Google TensorFlow，Torch and Tensor Processing Unit (TPU). In particular, scalable tensor methods have attracted considerable amount of attention, with successes in a series of learning tasks, such as learning latent variable models, relational learning, spatio-temporal forecasting and training deep neural networks.
These progresses trigger new directions and problems towards tensor methods in machine learning. The workshop aims to foster discussion, discovery, and dissemination of research activities and outcomes in this area and encourages breakthroughs. We will bring together researchers in theories and applications who are interested in tensors analysis and development of tensor-based algorithms. We will also invite researchers from related areas, such as numerical linear algebra, high-performance computing, deep learning, statistics, data analysis, and many others, to contribute to this workshop. We believe that this workshop can foster new directions, closer collaborations and novel applications. We also expect a deeper conversation regarding why learning with tensors at current stage is important, where it is useful, what tensor computation software and hardware work well in practice and, how we can progress further with interesting research directions and open problems.
|8:30 - 8:40||Openning Remarks||video|
|8:40 - 9:20||Invited Talk: Amnon Shashua||video|
|9:20 - 10:00||Contributed Talk||video|
|10:00 - 10:30||Poster Spotlight 1||video|
|10:30 - 11:00||Coffee Break and Poster Session 1|
|11:00 - 11:40||Invited Talk: Lek-Heng Lim||video|
|11:40 - 12:20||Invited Talk: Jimeng Sun||video|
|11:20 - 14:00||Lunch|
|14:00 - 14:40||Invited Talk: Gregory Valiant|
|14:40 - 15:00||Poster Spotlight 2|
|15:00 - 15:30||Coffee Break and Poster Session 2|
|15:30 - 16:10||Invited Talk: Vagelis Papalexias|
|16:10 - 17:00||PhD Symposium|
|17:00 - 18:00||Panel Discussion and Closing Remarks|
The Hebrew University of Jerusalem
Prof. Amnon Shashua holds the Sachs chair in computer science at the Hebrew University of Jerusalem. His field of expertise is computer vision and machine learning. For his academic achievements he received the MARR prize Honorable Mention in 2001, the Kaye innovation award in 2004, and the Landau award in exact sciences in 2005.
In 1999 Prof. Shashua co-founded Mobileye, an Israeli company developing a system-on-chip and computer vision algorithms for a driving assistance system, providing a full range of active safety features using a single camera. Today, approximately 11 million cars from 25 automobile manufacturers rely on Mobileye technology to make their vehicles safer to drive. In August 2014, Mobileye claimed the title for largest Israeli IPO ever, by raising $1B at a market cap of $5.3B. In addition, Mobileye is developing autonomous driving technology with more than a dozen car manufacturers. An early version of Mobileye’s autonomous driving technology was deployed in series as an "autopilot" feature in October, 2015, and will evolve to support more autonomous features in 2016 and beyond. The introduction of autonomous driving capabilities is of a transformative nature and has the potential of changing the way cars are built, driven and own in the future.
In 2010 Prof. Shashua co-founded OrCam which harnesses computer vision artificial intelligence to assist people who are visually impaired or blind. The OrCam MyEye device is unique in its ability to provide visual aid to hundreds of millions of people, through a discreet wearable platform. Within its wide-ranging scope of capabilities, OrCam’s device can read most texts (both indoors and outdoors) and learn to recognize thousands of new items and faces.
Georgia Institute of Technology
Jimeng Sun is an Associate Professor of School of Computational Science and Engineering at College of Computing at Georgia Institute of Technology. His research focuses on medical informatics, especially in applying large-scale predictive modeling and similarity analytics on biomedical applications.
Dr. Sun has extensive research records on data mining: big data analytics, similarity metric learning, social network analysis, predictive modeling, tensor analysis, and visual analytics. He also applies data mining to healthcare applications such as heart failure onset prediction and hypertension control management.
He has published over 70 papers, filed over 20 patents (5 granted). He has received ICDM best research paper in 2008, SDM best research paper in 2007, and KDD Dissertation runner-up award in 2008. Dr. Sun received his B.S. and M.Phil. in Computer Science from Hong Kong University of Science and Technology in 2002 and 2003, and PhD in Computer Science in Carnegie Mellon University in 2007. Prior to joining Georgia Tech, He was a research staff member at IBM TJ Watson Research Center.
University of Chicago
Lek-Heng Lim is an Assistant Professor in the Computational and Applied Mathematics Initiative, the Department of Statistics, and the College of University of Chicago. His research focuses on tensors and their coordinate representations, hypermatrices. He is interested in the hypermatrix equivalents of various matrix notions, their mathematical and computational properties, and their applications to science and engineering. Another area of Lim's interests is applied/computational algebraic and differential geometry, particularly Hodge Laplacians and geometry of subspaces. Lim is also generally interested in numerical linear algebra, optimization and machine learning.
Lim was educated at Stanford University (PhD), Cambridge University, Cornell University (MS), and the National University of Singapore (BS). Prior to joining the University of Chicago as an Assistant Professor, he was the Charles Morrey Assistant Professor at UC Berkeley. Lim serves on the editorial boards of Linear Algebra and its Applications and Linear and Multilinear Algebra. His work is supported by an AFOSR Young Investigator Award, an NSF Early Career Award, and a DARPA Young Faculty Award.
Greg Valiant is an Assistant Professor in the Computer Science Department at Stanford, after completing a postdoc at Microsoft Research, New England. His main research interests are in algorithms, learning, applied probability and statistics; he is also interested in game theory, and has enjoyed working on problems in database theory.
Valiant graduated from Harvard with a BA in Math and an MS in Computer Science, and obtained his PhD in Computer Science from UC Berkeley in 2012.
Evangelos (Vagelis) Papalexakis is an Assistant Professor of the CSE Dept. at University of California Riverside. He obtained his PhD degree at the School of Computer Science at Carnegie Mellon University (CMU), under the supervision of Prof. Christos Faloutsos since August 2011. Prior to joining CMU, he obtained his Diploma and MSc in Electronic & Computer Engineering at the Technical University of Crete, in Greece.
Broadly, his research interests span the fields of Data Mining, Machine Learning, and Signal Processing. His research involves designing scalable algorithms for mining large multi-aspect datasets, with specific emphasis on tensor factorization models, and applying those algorithms to a variety of real world multi-aspect data problems. His work has appeared in KDD, ICDM, SDM, ECML-PKDD, WWW, PAKDD, ICDE, ICASSP, IEEE Transactions of Signal Processing, and ACM TKDD. He has a best student paper award at PAKDD'14, finalist best papers for SDM'14 and ASONAM'13 and he was a finalist for the Microsoft PhD Fellowship and the Facebook PhD Fellowship. Besides his academic experience at CMU, he has industrial research experience working at Microsoft Research Silicon Valley during the summers of 2013 and 2014 and Google Research during the summer of 2015.
Papers submitted to the workshop should be up to four pages long excluding references and in NIPS 2016 format. As the review process is not blind, authors can reveal their identity in their submissions. All inquiries could be sent to firstname.lastname@example.org.
Submissions page: Tensor-Learn 2016.
Note on open problem submissions: In order to promote new and innovative research on tensors, we plan to accept a small number of high quality manuscripts describing open problems in tensor learning. Such papers should provide a clear, detailed description and analysis of a new or open problem that poses a significant challenge to existing techniques, as well as a thorough empirical investigation demonstrating that current methods are insufficient. Accepted submissions will be presented as posters. there is no published proceedings and the authors are free to send it elsewhere.
Paper Submission Deadline:
Oct 28, 2016, 11:59 PM PST
Nov 7, 2016, 11:59 PM PST
Nov 25, 2016, 11:59 PM PST
Workshop: December 10, 2016