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Nowhere in Andrew Ng’s presentation does he mention how to use unsupervised learning, but he does contain a brief discussion about “Synthesis.” Such synthesis suggestions are all about blending pre-existing information or using a rendering engine to augment your instruction set.

The second upcoming paper from Raquel’s group is on employing Deep Learning for Dense Optical Flow, in the spirit of FlowNet , which I discussed in my ICCV 2015 hottest papers blog post The approach is built on the observation that the scene is usually composed of a static background, as nicely as a relatively tiny number of site visitors participants which move rigidly in 3D. The dense optical flow method is applied to autonomous driving.

Today’s post contains a short introduction to SLAM, a detailed description of what happened at the workshop (with summaries of all 7 talks), and some take-home messages from the Deep Studying-focused panel discussion at the end of the session.