No more seats available. We will record the talks and make the videos public after the workshop.

The workshop aims at bringing together leading scientists in deep learning and related areas within machine learning, artificial intelligence, mathematics, statistics, and neuroscience. No formal submission is required. Speakers are invited to present their recently published work as well as work in progress, and to share their vision and perspectives for the field.

Program

March 19th

Kunihiko Fukushima (Fuzzy Logic Systems Institute): Artificial Vision by Deep CNN Neocognitron
Moustapha Cisse (Facebook AI Research): Deep Learning in the Land of Adversity
Gang Niu (The University of Tokyo and RIKEN-AIP): When Deep Learning Meets Weakly-Supervised Learning
Babak Shahbaba (UC Irvine): Decoding of Hippocampal Neural Activity Using Deep Learning Methods Reveals Predictive Activation of Upcoming Sequence of Events
Jun Zhu (Tsinghua University): ZhuSuan: A Probabilistic Programming Library for Bayesian Deep Learning
Mohammad Emtiyaz Khan (RIKEN-AIP): Uncertainty through the Optimizer: Bayesian Deep Learning via Perturbed Adaptive Learning-Rate Methods
Bob Williamson (Australian National University): Information Processing Equalities
Klaus-Robert Mueller (TU Berlin): Machine Learning for the Sciences (tentative)
Eunho Yang (KAIST): Reliable Predictions for Healthcare

March 20th

Tom Schaul (DeepMind): Deep Reinforcement Learning
Wee Sun Lee (National University of Singapore): Planning with Deep Neural Networks
Naoaki Okazaki (Tokyo Institute of Technology): Generating Text with Deep Neural Networks
Kevin Murphy (Google Research): Generative Models for Language and Vision

March 21st

Shun-ichi Amari (RIKEN Brain Science Institute): Statistical Neurodynamics of Deep Networks
Pradeep Ravikumar (Carnegie Mellon University): Destructive Deep Learning
Sumio Watanabe (Tokyo Institute of Technology): Cross Validation and WAIC in Layered Neural Networks
Le Song (Georgia Tech and Ant Financial): Enhancing Deep Learning with Structures
Pierre Baldi (University of California, Irvine): Neural Network Complexity: Shallow versus Deep
Taiji Suzuki (The University of Tokyo and RIKEN-AIP): Generalization Error and Compressibility of Deep Learning via Kernel Analysis
Li Erran Li (Uber ATG and Columbia University): 3D Objection Detection: Recent Advances and Future Directions
Amir Globerson (Tel Aviv University and Google): How SGD Can Succeed Despite Non-Convexity and Over-Specification
Sho Sonoda (Waseda University): Transport Analysis of Denoising Autoencoder
Yanghua Jin (Fudan University): Creating Anime Characters with GAN

March 22nd

Tatsuya Harada (The University of Tokyo and RIKEN-AIP): Learning Deep Neural Networks from Limited Examples
Jan Peters (TU Darmstadt & MPI-IS): Policy Search with the f-Divergence
Masaaki Imaizumi (The Institute of Statistical Mathematics): Statistical Estimation for Non-Smooth Functions by Deep Neural Networks
Jean-Philippe Vert (ENS Paris): Supervised Quantile Normalization
Akiko Takeda (The Institute of Statistical Mathematics): Efficient DC Algorithm for Nonconvex Nonsmooth Optimization Problems
James Kwok (Hong Kong University of Science and Technology): TBA
Edgar Simo-Serra (Waseda University): Semi-Supervised Learning of Sketch Simplification
Takayuki Osogami (IBM Research AI): Dynamic Determinantal Point Processes
Masatoshi Hamanaka (RIKEN AIP): Music Structure Analysis based on Deep Learning