Schedule

Schedule may change, please check for any updates before 11 Dec.

SCHEDULE


Room: 513ef


8:50 9:15 Dmitry Storcheus, A Survey of Modern Questions and Challenges in Feature Extraction

9:15 10:00 Fei Sha, Do shallow kernel methods match deep neural networks - and if not, what can the shallow ones learn from the deep ones?

10:00 10:20 James Y. Zou, Discovering Salient Features via Adaptively Chosen Comparisons  

10:20 10:30 Coffee Break


10:30 11:15 Michael Mahoney, Column Subset Selection on Terabyte-sized Scientific Data

11:15 12:00 Le Song, Scalable Kernel Methods for Big Nonlinear Problems

12:00 12:20 Yixuan Li, Convergent Learning: Do different neural networks learn similar features?

12:20 14:50 Poster Session 1

12:30 14:50 Lunch Break


14:50 15:35 Kilian Weinberger, Deep Manifold Traversal

15:35 16:20 Grégoire Montavon and Wojciech Samek (joint work with Klaus-Robert Müller), Explaining individual deep network predictions and measuring the quality of these explanations

16:20 16:30 Coffee Break

16:30 17:10 Panel DIscussion

17:10 18:30 Poster Session 2