Schedule

           

 

Monday, 22   |   Tuesday, 23   |   Wednesday, 24   |   Thursday, 25   |   Friday, 26
Change to table view

Location/Room: (P1) = Poludniowa 1, (P2) = Poludniowa 2, (W) = Wschodnia



Monday, 22


08:00-08:45

Reception

08:45-09:00

Location: P1 → Welcome

09:00-10:30

Location: P1, Tomas Mikolov - Facebook ⊳ “Using Neural Networks for Modeling and Representing Natural Languages (with Piotr Bojanowski and Armand Joulin)”     introductory

Location: P2, Björn Schuller - Imperial College London ⊳ “Deep Learning for Intelligent Signal Processing”     introductory/intermediate

11:00-12:30

Location: P1, Alex Smola - Amazon ⊳ “Dive into Deep Learning”     introductory

Location: P2, Ponnuthurai N Suganthan - Nanyang Technological University ⊳ “Learning Algorithms for Classification, Forecasting and Visual Tracking”     introductory/intermediate

Location: W, Bertrand Thirion - INRIA ⊳ “Understanding the Brain with Machine Learning”     introductory

12:30-13:30

Lunch

13:30-15:00

Location: P1, Aaron Courville - University of Montréal ⊳ “Deep Generative Models”     introductory/intermediate

Location: W, Issam El Naqa - University of Michigan ⊳ “Deep Learning for Biomedicine”     introductory/intermediate

Location: P2, Sergei V. Gleyzer - University of Florida ⊳ “Feature Extraction, End-end Deep Learning and Applications to Very Large Scientific Data: Rare Signal Extraction, Uncertainty Estimation and Realtime Machine Learning Applications in Software and Hardware”     introductory/intermediate

15:30-17:00

Location: P1, Tomas Mikolov - Facebook ⊳ “Using Neural Networks for Modeling and Representing Natural Languages (with Piotr Bojanowski and Armand Joulin)”     introductory

Location: P2, Björn Schuller - Imperial College London ⊳ “Deep Learning for Intelligent Signal Processing”     introductory/intermediate

17:30-19:00

Location: P1, Alex Smola - Amazon ⊳ “Dive into Deep Learning”     introductory

Location: P2, Ponnuthurai N Suganthan - Nanyang Technological University ⊳ “Learning Algorithms for Classification, Forecasting and Visual Tracking”     introductory/intermediate

Location: W, Bertrand Thirion - INRIA ⊳ “Understanding the Brain with Machine Learning”     introductory



Tuesday, 23


08:00-08:45

Location: W → Open session I

09:00-10:30

Location: P1, Aaron Courville - University of Montréal ⊳ “Deep Generative Models”     introductory/intermediate

Location: W, Issam El Naqa - University of Michigan ⊳ “Deep Learning for Biomedicine”     introductory/intermediate

Location: P2, Sergei V. Gleyzer - University of Florida ⊳ “Feature Extraction, End-end Deep Learning and Applications to Very Large Scientific Data: Rare Signal Extraction, Uncertainty Estimation and Realtime Machine Learning Applications in Software and Hardware”     introductory/intermediate

11:00-12:30

Location: P1, Tomas Mikolov - Facebook ⊳ “Using Neural Networks for Modeling and Representing Natural Languages (with Piotr Bojanowski and Armand Joulin)”     introductory

Location: P2, Björn Schuller - Imperial College London ⊳ “Deep Learning for Intelligent Signal Processing”     introductory/intermediate

12:30-13:30

Lunch

13:30-15:00

Location: P1, Alex Smola - Amazon ⊳ “Dive into Deep Learning”     introductory

Location: P2, Ponnuthurai N Suganthan - Nanyang Technological University ⊳ “Learning Algorithms for Classification, Forecasting and Visual Tracking”     introductory/intermediate

Location: W, Bertrand Thirion - INRIA ⊳ “Understanding the Brain with Machine Learning”     introductory

15:30-17:00

Location: P1, Aaron Courville - University of Montréal ⊳ “Deep Generative Models”     introductory/intermediate

Location: W, Issam El Naqa - University of Michigan ⊳ “Deep Learning for Biomedicine”     introductory/intermediate

Location: P2, Sergei V. Gleyzer - University of Florida ⊳ “Feature Extraction, End-end Deep Learning and Applications to Very Large Scientific Data: Rare Signal Extraction, Uncertainty Estimation and Realtime Machine Learning Applications in Software and Hardware”     introductory/intermediate

17:30-19:00

Location: P2, Qiang Ji - Rensselaer Polytechnic Institute ⊳ “Probabilistic Deep Learning for Computer Vision”     introductory/intermediate

Location: P1, Zhongfei Zhang - Binghamton University ⊳ “Knowledge Discovery from Complex Data with Deep Learning”     introductory/advanced

19:15-20:15

Location: P1 → Keynote Balcan

20:30 - ...

Dinner with strangers


Wednesday, 24


08:00-08:45

Location: W → Industrial session I

09:00-10:30

Location: W, Hermann Ney - RWTH Aachen University ⊳ “Speech Recognition and Machine Translation: From Statistical Decision Theory to Machine Learning and Deep Neural Networks”     intermediate/advanced

Location: P2, Fabio Roli - University of Cagliari ⊳ “Adversarial Machine Learning”     introductory/intermediate

Location: P1, Johan Suykens - KU Leuven ⊳ “Deep Learning, Neural Networks and Kernel Machines”     introductory/intermediate

11:00-12:30

Location: P2, Qiang Ji - Rensselaer Polytechnic Institute ⊳ “Probabilistic Deep Learning for Computer Vision”     introductory/intermediate

Location: P1, Zhongfei Zhang - Binghamton University ⊳ “Knowledge Discovery from Complex Data with Deep Learning”     introductory/advanced

12:30-13:30

Lunch

13:30-15:00

Location: W, Hermann Ney - RWTH Aachen University ⊳ “Speech Recognition and Machine Translation: From Statistical Decision Theory to Machine Learning and Deep Neural Networks”     intermediate/advanced

Location: P2, Fabio Roli - University of Cagliari ⊳ “Adversarial Machine Learning”     introductory/intermediate

Location: P1, Johan Suykens - KU Leuven ⊳ “Deep Learning, Neural Networks and Kernel Machines”     introductory/intermediate

15:30-17:00

Location: P2, Qiang Ji - Rensselaer Polytechnic Institute ⊳ “Probabilistic Deep Learning for Computer Vision”     introductory/intermediate

Location: P1, Zhongfei Zhang - Binghamton University ⊳ “Knowledge Discovery from Complex Data with Deep Learning”     introductory/advanced

17:30-19:00

Employer session

19:15-20:15

Location: P1 → Keynote Gales

20:30 - ...

Dinner with strangers


Thursday, 25


08:00-08:45

Location: W → Open session II

09:00-10:30

Location: P2, Gaël Varoquaux - INRIA ⊳ “Representation Learning in Limited Data Settings”     intermediate

Location: P1, René Vidal - Johns Hopkins University ⊳ “Mathematics of Deep Learning”     intermediate/advanced

Location: W, Haixun Wang - WeWork ⊳ “Abstractions, Concepts, and Machine Learning”     intermediate

11:00-12:30

Location: W, Hermann Ney - RWTH Aachen University ⊳ “Speech Recognition and Machine Translation: From Statistical Decision Theory to Machine Learning and Deep Neural Networks”     intermediate/advanced

Location: P2, Fabio Roli - University of Cagliari ⊳ “Adversarial Machine Learning”     introductory/intermediate

Location: P1, Johan Suykens - KU Leuven ⊳ “Deep Learning, Neural Networks and Kernel Machines”     introductory/intermediate

12:30-13:30

Lunch

13:30-15:00

Location: W, James Kwok - Hong Kong University of Science and Technology ⊳ “Compressing Neural Networks”     introductory/intermediate

Location: P2, Jose C. Principe - University of Florida ⊳ “Cognitive Architectures for Object Recognition in Video”     intermediate/advanced

Location: P1, Sargur Srihari - University at Buffalo ⊳ “Explainable Artificial Intelligence”     intermediate/advanced

15:30-17:00

Location: P2, Gaël Varoquaux - INRIA ⊳ “Representation Learning in Limited Data Settings”     intermediate

Location: P1, René Vidal - Johns Hopkins University ⊳ “Mathematics of Deep Learning”     intermediate/advanced

Location: W, Haixun Wang - WeWork ⊳ “Abstractions, Concepts, and Machine Learning”     intermediate

17:30-19:00

Location: W, Vasant Honavar - Pennsylvania State University ⊳ “Causal Models for Making Sense of Data”     introductory/intermediate

Location: P1, Xiaowei Xu - University of Arkansas, Little Rock ⊳ “Multi-resolution Models for Learning Multilevel Abstract Representations of Text”     introductory/advanced

Location: P2, Ming-Hsuan Yang - University of California, Merced ⊳ “Learning to Track Objects”     intermediate/advanced

19:15-20:15

Location: P1 → Keynote van der Schaar

20:30 - ...

Dinner with strangers


Friday, 26


08:00-08:45

Location: W → Industrial session II

09:00-10:30

Location: W, James Kwok - Hong Kong University of Science and Technology ⊳ “Compressing Neural Networks”     introductory/intermediate

Location: P2, Jose C. Principe - University of Florida ⊳ “Cognitive Architectures for Object Recognition in Video”     intermediate/advanced

Location: P1, Sargur Srihari - University at Buffalo ⊳ “Explainable Artificial Intelligence”     intermediate/advanced

11:00-12:30

Location: P2, Gaël Varoquaux - INRIA ⊳ “Representation Learning in Limited Data Settings”     intermediate

Location: P1, René Vidal - Johns Hopkins University ⊳ “Mathematics of Deep Learning”     intermediate/advanced

Location: W, Haixun Wang - WeWork ⊳ “Abstractions, Concepts, and Machine Learning”     intermediate

12:30-13:30

Lunch

13:30-15:00

Location: W, Vasant Honavar - Pennsylvania State University ⊳ “Causal Models for Making Sense of Data”     introductory/intermediate

Location: P1, Xiaowei Xu - University of Arkansas, Little Rock ⊳ “Multi-resolution Models for Learning Multilevel Abstract Representations of Text”     introductory/advanced

Location: P2, Ming-Hsuan Yang - University of California, Merced ⊳ “Learning to Track Objects”     intermediate/advanced

15:30-17:00

Location: W, James Kwok - Hong Kong University of Science and Technology ⊳ “Compressing Neural Networks”     introductory/intermediate

Location: P2, Jose C. Principe - University of Florida ⊳ “Cognitive Architectures for Object Recognition in Video”     intermediate/advanced

Location: P1, Sargur Srihari - University at Buffalo ⊳ “Explainable Artificial Intelligence”     intermediate/advanced

17:30-19:00

Location: W, Vasant Honavar - Pennsylvania State University ⊳ “Causal Models for Making Sense of Data”     introductory/intermediate

Location: P1, Xiaowei Xu - University of Arkansas, Little Rock ⊳ “Multi-resolution Models for Learning Multilevel Abstract Representations of Text”     introductory/advanced

Location: P2, Ming-Hsuan Yang - University of California, Merced ⊳ “Learning to Track Objects”     intermediate/advanced