Deep Learning: Theory, Algorithms, and Applications 2018, March 19-22
http://www.ms.k.u-tokyo.ac.jp/TDLW2018/
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.
*Publication of 10. and 20. is undecided.
01.Opening: Masashi Sugiyama
02.Kunihiko Fukushima: Artificial Vision by Deep CNN Neocognitron
03.Moustapha Cisse: Deep Learning in the Land of Adversity
04.Gang Niu: When Deep Learning Meets Weakly-Supervised Learning
05.Babak Shahbaba: Decoding of Hippocampal Neural Activity Using Deep Learning Methods Reveals Predictive Activation of Upcoming Sequence of Events
06.Jun Zhu: ZhuSuan:A Probabilistic Programming Library for Bayesian Deep Learning
07.Mohammad Emtiyaz Khan: Uncertainty through the Optimizer: Bayesian Deep Learning via Perturbed Adaptive Learning-Rate Methods
08.Bob Williamson: Information Processing Equalities
09.Klaus-Robert Mueller: Machine Learning for the Sciences (tentative)
11.Akira Naruse: All You Need is Fast Dense Matrix Multiply?
12.Tom Schaul: Deep Reinforcement Learning
13.Wee Sun Lee: Planning with Deep Neural Networks
14.Naoaki Okazaki: Generating Text with Deep Neural Networks
15.Kevin Murphy: Generative Models for Language and Vision
16.Shun-ichi Amari: Statistical Neurodynamics of Deep Networks
17.Pradeep Ravikumar: Destructive Deep Learning
18.Sumio Watanabe: Cross Validation and WAIC in Layered Neural Networks
19.Le Song: Enhancing Deep Learning with Structures
21.Taiji Suzuki: Generalization Error and Compressibility of Deep Learning via Kernel Analysis
22.Li Erran Li: 3D Objection Detection: Recent Advances and Future Directions
23.Amir Globerson: How SGD Can Succeed Despite Non-Convexity and Over-Specification
24.Sho Sonoda: Transport Analysis of Denoising Autoencoder
25.Yanghua Jin: Creating Anime Characters with GAN
26.Kenichi Narioka: Deep Learning Makes Road Safer
27.Tatsuya Harada: Learning Deep Neural Networks from Limited Examples
28.Jan Peters: Policy Search with the f-Divergence
29.Masaaki Imaizumi: Statistical Estimation for Non-Smooth Functions by Deep Neural Networks
30.Jean-Philippe Vert: Supervised Quantile Normalization
31.Akiko Takeda: Efficient DC Algorithm for Nonconvex Nonsmooth Optimization Problems
32.James Kwok: Compressed Deep Neural Networks
33.Edgar Simo-Serra: Semi-Supervised Learning of Sketch Simplification
34.Takayuki Osogami: Dynamic Determinantal Point Processes
35.Masatoshi Hamanaka: Music Structure Analysis based on Deep Learning
36.Shinji Nakadai: Four Waves of AI Business -NEC the WISE and NEXT
