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#1
Oriol Vinyals, Google
#2
Jason Weston, Hashtags, Clicks and Likes: Supervision for Content-based Posts
#3
Jason Weston, Facebook
#4
Ryan Adams: Designing Molecules with Deep Learning and Bayesian Optimization
#5
From STDP towards Biologically Plausible Deep Learning
#6
Large-scale Bayesian Multi-label Learning via Positive Labels Only (Piyush Rai, Changwei Hu, Ricardo Henao and Lawrence Carin)
#7
Aurelien Bellet, The Frank-Wolfe Algorithm: Recent Results and Applications to High-Dimensional Similarity Learning and Distributed Optimization
#8
Locally Non-linear Embeddings for Extreme Multi-Label Learning (Kush Bhatia, Himanshu Jain, Purushottam Kar, Prateek Jain and Manik Varma)
#9
Consistent Label Tree Classifiers for Extreme Multi-Label Classification (Kalina Jasinska and Krzysztof Dembczynski)
#10
John Langford, The Elusive Theory of Efficient Classification
#11
John Myles White: Julia's Approach to Open Source Machine Learning
#12
Guy Lever, Greedy Function Approximation for Efficient Non-Parametric Approaches to Reinforcement Learning
#13
Sequential Dynamic Classification for Large Scale Multiclass Problems (Raphael Puget, Nicolas Baskiotis and Patrick Gallinari)
#14
Vitaly Kuznetsov, Ensemble methods for structured prediction
#15
Ludovic Denoyer and Patrick Gallinari, Deep Sequential Neural Networks
#16
Matthew Rocklin: Extending the Numeric Python ecosystem beyond in-memory computing
#17
Intro
#18
Manik Varma, Extreme Classification: A New Paradigm for Ranking & Recommendation
#19
Tara Sainath, Google
#20
BLOG: a probabilistic programming language for open-universe contingent Bayesian networks
#21
Coffee Break
#22
Panel Discussion (topic: Future of Deep Learning)
#23
Karol Gregor, Google DeepMind
#24
Ian Goodfellow, Google
#25
Philippe Hamel, Google, To Have a Tiger by the Tail: Improving Music Recommendation for International Users
#26
Roland Memisevic, University of Montreal
#27
Yoshua Bengio
#28
Massively Parallel Methods for Deep Reinforcement Learning
#29
David Duvenaud (Harvard), Automatically constructing models, and automatically explaining them, too.
#30
Juergen Schmidhuber, PowerPlay: training an increasingly general (reinforcement learning) problem solver by continually searching for the simplest still unsolvable problem
#31
Bertrand Thirion, INRIA, Learning representations from functional brain images
#32
Ruslan Salakhutdinov, Learning Recurrent Attention Models
#33
Sander Dieleman, Ghent University, Deep Content-Based Music Recommendation
#34
Juergen Schmidhuber (IDSIA), Recursive Self-Improvement
#35
Panel Discussion: Marc Boulle, Rich Caruana, David Duvenaud, Matthew Hoffmann, Juergen Schmidhuber, Michele Sebag, Joaquin Vanschoren.
#36
Tutorial. Marc Bellemare, The Atari Learning Environment
#37
Invited Talk. David Silver
#38
Ben Glocker, Imperial College London, Semantic Imaging: Learning to Understand Medical Images
#39
Neil Lawrence, Sheffield University
#40
Michele Sebag (CNRS), Algorithm Recommendation as Collaborative Filtering
#41
Human-Guided Learning of Social Action Selection for Robot-Assisted Therapy, Emmanuel Senft, Paul Baxter and Tony Belpaeme
#42
Neil Lawrence, Large Scale Learning in Gaussian Processes
#43
Nonlinear Hebbian learning as a universal principle in unsupervised feature learning
#44
Orhan Firat, Learning Deep Temporal Representations for fMRI Brain Decoding
#45
Bob L. Sturm, Queen Mary University of London, The Scientific Evaluation of Music Content Analysis Systems: Valid Empirical Foundations for Future Real-World Impact
#46
Rajesh Ranganath, Princeton University
#47
Eleni Pratsini, Using Big Mobile Data to Analyze Social Events in Cities
#48
Francis Bach, Sharp analysis of random feature expansions
#49
Ruslan Salakhutdinov, Deep Multimodal Learning: From Caption Generation to Zero-Shot Learning
#50
Diederik Roijers, Shimon Whiteson, Peter Vamplew and Richard Dazeley, Why Multi-objective Reinforcement Learning?
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