June 11, 2018 15:52

Deep Learning: Theory, Algorithms, and Applications 2018, March 19-22


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