Workshop Program

08:30-08:40 Opening Remarks

08:40-09:20 Invited Talk 1:
Representation learning in vision and neuroscience

            Prof. Yingnian Wu (UCLA), [slides]
09:20-10:00 Invited Talk 2:
Geometric View of Optimal Transportation and Generative Adversarial Networks

            Prof. Xianfeng Gu (Stony Brook University and Harvard University), [slides][abstract]

10:00-10:40 Coffee Break and Poster Session

10:40-11:20 Invited Talk 3:
Universal Features – Information Extraction for Transfer Learning

            Prof. Lizhong Zheng (MIT), [slides][abstract]
11:20-11:40 Contributed Talk 1:
Function Norms for Neural Networks

            Amal Rannen Triki (KU Leuven), [paper][supp][slides]
11:40-12:00 Contributed Talk 2:
Conditional Recurrent Flow: Conditional Generation of Longitudinal Samples with Applications to Neuroimaging

            Seong Jae Hwang (University of Wisconsin-Madison), [paper][slides]

12:00-13:30 Lunch and Break

13:30-14:10 Invited Talk 4:
Complete Dictionary Learning via L4-Norm Maximization over the Orthogonal Group

            Prof. Yi Ma (UCB), [slides][abstract]
14:10-14:50 Invited Talk 5:
Deep Networks Architectures

            Prof. Alan L. Yuille (Johns Hopkins University), [abstract]

14:50-15:30 Coffee Break and Poster Session

15:30-15:50 Contributed Talk 3:
Adaptive Confidence Smoothing for Generalized Zero-Shot Learning

            Yuval Atzmon (Bar-Ilan University, NVIDIA Research), [paper] [slides]
15:50-16:10 Contributed Talk 4:
UGLLI Face Alignment: Estimating Uncertainty with Gaussian Log-Likelihood Loss

            Abhinav Kumar (University of Utah), [paper][slides]

16:10-16:50 Invited Talk 6:
Evaluation of Deep Generative Models

            Mehdi Sajjadi (Google Research), [slides]
16:50-17:30 Poster Session and Discussion


Accepted Papers

1. SVQN: Sequential Variational Soft Q-Learning Networks
          Huang, Shiyu; Su, Hang; Zhu, Jun; Chen, Ting [paper][poster]

2. Squeezed Bilinear Pooling for Fine-Grained Visual Categorization
          Liao, Qiyu; Xu, Min; Wang, Dadong; Holewa, Hamish [paper][poster]

3. Learning to Find Correlated Features by Maximizing Information Flow in Convolutional Neural Networks
          Shen, Wei; Li, Fei; Liu, Rujie [paper][poster]

4. Exploring Dynamic Routing As A Pooling Layer
          Zhao, Lei; Huang, Lei [paper][poster]

5. Interpreting Intentionally Flawed Models with Linear Probes
          Graziani, Mara; Müller, Henning; Andrearczyk, Vincent [paper][poster]

6. (Oral paper) Function Norms for Neural Networks
          Rannen Triki, Amal; Berman, Maxim; Kolmogorov, Vladimir; Blaschko, Matthew [paper][supp][poster][slides]

7. Open Set Recognition through Deep Neural Network Uncertainty: Does Out-of-Distribution Detection Require Generative Classifiers?
          Mundt, Martin; Pliushch, Iuliia; Majumder, Sagnik; Ramesh, Visvanathan [paper][poster]

8. Direct Validation of the Information Bottleneck Principle for Deep Nets
          Elad, Adar; Haviv, Doron; Blau, Yochai; Michaeli, Tomer [paper][supp][poster]

9. Lautum Regularization for Semi-supervised Transfer Learning
          Jakubovitz, Daniel; Rodrigues, Miguel; Giryes, Raja [paper][supp][poster]

10. A Novel Adversarial Inference Framework for Video Prediction
          Hu, Zhihang; Wang, Jason T. L. [paper][poster]

11. (Oral paper) Conditional Recurrent Flow: Conditional Generation of Longitudinal Samples with Applications to Neuroimaging
          Hwang, Seong Jae; Tao, Zirui; Singh, Vikas; Kim, Won Hwa [paper][poster][slides]

12. Efficient priors for scalable variational inference in Bayesian deep neural networks
          Krishnan, Ranganath*;Subedar, Mahesh*; Tickoo, Omesh [paper][poster]

13. (Oral paper) Adaptive Confidence Smoothing for Generalized Zero-Shot Learning
          Atzmon, Yuval; Chechik, Gal [paper] [slides]

14. (Oral paper) UGLLI Face Alignment: Estimating Uncertainty with Gaussian Log-Likelihood Loss
          Kumar, Abhinav; Mou, Wenxuan; Marks, Tim K; Feng, Chen; Liu, Xiaoming [paper][slides][poster]

15. OpenVINO Deep Learning Workbench: Comprehensive Analysis and Tuning of Neural Networks Inference
          Demidovskij, Alexander; Gorbachev, Yury; Fedorov, Mikhail; Slavutin, Iliya; Tugarev, Artyom; Fatekhov, Marat; Tarkan, Yaroslav [paper][poster]

16. Stochastic Relational Network
          Yoo, Kang Min; Cho, Hyunsoo; Lee, Hanbit; Han, Jeeseung; Lee, Sang-goo [paper][poster]

17. On the Geometry of Rectifier Convolutional Neural Networks
          Gamba, Matteo; Azizpour, Hossein; Carlsson, Stefan; Bjorkman, Marten [paper][poster]

18. Tackling Disturbed Depth Maps by Learning Input Data Confidence
          Eldesokey, Abdelrahman; Persson, Mikael; Felsberg, Michael; Shahbaz Khan, Fahad [paper][poster]

19. Attack Agnostic Method For Adversarial Detection
          Saha, Sambuddha [paper][poster]