https://vadl2017.github.io/ Recent content on VADL 2017: Workshop on Visual Analytics for Deep Learning Hugo -- gohugo.io en-us Released under the MIT license Thu, 13 Apr 2017 14:56:02 -0400 https://vadl2017.github.io/schedule/index/ Thu, 13 Apr 2017 14:56:02 -0400 https://vadl2017.github.io/schedule/index/ Hello world Hello. https://vadl2017.github.io/test/index/ Thu, 13 Apr 2017 15:26:17 -0400 https://vadl2017.github.io/test/index/ Hello who are you https://vadl2017.github.io/test/test/ Thu, 13 Apr 2017 15:32:32 -0400 https://vadl2017.github.io/test/test/ Are you crzay? https://vadl2017.github.io/call-for-papers/index/ Thu, 13 Apr 2017 14:56:14 -0400 https://vadl2017.github.io/call-for-papers/index/ https://vadl2017.github.io/organizers/index/ Thu, 13 Apr 2017 14:55:28 -0400 https://vadl2017.github.io/organizers/index/ https://vadl2017.github.io/index/ Tue, 11 Apr 2017 12:11:22 -0400 https://vadl2017.github.io/index/ Call for Papers Recently, deep neural networks have been achieving breakthroughs in various major artificial intelligence tasks such as machine translation, image understanding, speech recognition, and so on. In these tasks, deep neural networks reached the level of an accuracy comparable to or even better than humans’ performance. However, understanding the deep neural networks is challenging due to their complicated inner workings. VADL, the workshop on visual analytics for deep learning, is a half-day workshop held in conjunction with IEEE VIS 2017 in Phoenix, AZ.