Deep Learning for Formal Reasoning
Deep learning has transformed machine perception in the past five years. However, recognizing patterns is a crucial feature of intelligence in general. Here we give a short overview on how deep learning can be utilized for formal reasoning, especially for reasoning in large mathematical theories. The fact that pattern recognition capabilities are essential for these tasks has wider implications for other tasks like software synthesis and long term planning in complicated environments. Here is will give a short overview on some methods that leverage deep learning for such tasks.
Christian Szegedy is research scientist at Google, working on deep learning for computer vision, including image recognition, object detection and video analysis. He is the designer of the Inception architecture which set new state of the art on the latest ImageNet benchmark in the latest Large Scale Visual Recognition Competition. Before joining Google in 2010, he was scientist at Cadence Research Laboratories in Berkeley devising algorithms for chip design. His background is discrete mathematics and mathematical optimization. Christian got his PhD from the University of Bonn in applied mathematics in 2005.