In applying the adversarial training, this paper adopts distributed word representation, or word embedding, as the input, rather than the traditional one-hot representation. The reason lies in the fact that the higher dimensionality the input has, the more likely it is to be disturbed by noise.
We explore top-notch Swiss AI facilities: starting with deep learning and neural network research at IDSIA in Lugano, to interdisciplinary research at École Polytechnique Fédérale de Lausanne and University of Basel, and ending with robotics innovations at ETH in Zurich and University of Zurich.
In 2015, Anh Nguyen published a paper in CVPR that identified a limit in computer vision, where you can fool a deep neural network (DNN) by changing an image in a way that’s imperceptible to humans, but can cause the DNN to label the image as something else entirely.
From May 14 to 18, the 30th International Joint Conference on Neural Networks (IJCNN 2017) was held in Anchorage, AK, USA. Continuing the long tradition, the conference is organized by the International Neural Network Society (INNS), in cooperation with the IEEE Computational Intelligence Society (IEEE-CIS).
On March 29, 2017, during the 2017 YunQi Computing Conference held in Shenzhen, Alibaba Cloud’s Chief Science Officer Dr Jingren Zhou officially launched the updated version of its machine learning platform “PAI 2.0”, intending to drastically reduce the technical threshold and development cost for AI.