Title: Prediction and Planning Under Uncertainty: The Case of Autonomous Driving
Abstract: In order to achieve a well specified goal an agent may use two distinct approaches: trial & error, or careful planning. In the first case the agent has to fail multiple times before learning a task (e.g. playing a card game), in the second we leverage the knowledge of the environment to avoid any fatal failure (e.g. vehicle collision).
Autonomous driving relies on accurate planning, which requires a good model of the world that also considers other vehicles' future response to our own actions. Effectively learning to predict such response, stochastic by nature, is the key aspect to successfully obtain planning under uncertainty.
Bio: Alfredo Canziani is a Post-Doctoral Deep Learning Research Scientist and Lecturer at NYU Courant Institute of Mathematical Sciences, under the supervision of professors KyungHyun Cho and Yann LeCun. His research mainly focusses on Machine Learning for Autonomous Driving. He has been exploring deep policy networks actions uncertainty estimation and failure detection, and long term planning based on latent forward models, which nicely deal with the stochasticity and multimodality of the surrounding environment. Alfredo obtained both his Bachelor (2009) and Master (2011) degrees in EEng cum laude at Trieste University, his MSc (2012) at Cranfield University, and his PhD (2017) at Purdue University. In his spare time, Alfredo is a professional musician, dancer, and cook, and keeps expanding his free online video course on Deep Learning, Torch, and PyTorch.
Alfredo Canziani (NYU)
Musician, math lover, cook, dancer, and lecturer who is currently working on predictive networks as PostDoc at NYU under Yann LeCun
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