Deep learning has rapidly moved from research to be a key component in providing industrial impact in areas such as autonomous driving. From initial semantic segmentation to more recent advanced systems, these algorithms continuously increase the consumption of data and computational resources. The amount of data being acquired, and the need of annotations keep growing exponentially; this opens up new challenges to improve accuracy and to achieve the desired safety level. In this talk, I will explore some of these challenges along with our proposed solutions in terms of active learning and computational efficiency for training and deploying deep networks at scale.
José M. Álvarez is a Senior Research Scientist at NVIDIA leading the research efforts in the AI-Infra team. The focus of the team is to scale-up deep learning for Autonomous driving. Previously, he was a senior research scientist at Toyota Research Institute and a senior scientist at Data61/CSIRO (formerly NICTA), Australia, working on deep learning for large scale dynamic scene understanding. Jose M. Alvarez graduated with his Ph.D. in October 2010, with a focus on robust road detection under real-world driving conditions. Dr. Alvarez did research stays at the University of Amsterdam (in 2008 and 2009) and the Electronics Research Group at Volkswagen (in 2010) and Boston College. Subsequently, he worked as a postdoctoral researcher at New York University under the supervision of Prof. Yann LeCun. Since 2014, he serves as an associate editor for IEEE Transactions on Intelligent Transportation Systems.
We will be raffling off an NVIDIA Jetson Nano!