The technology company is supporting the research collaboration on AI initiated by the Max Planck Society
Cyber Valley has attracted another collaboration partner from industry. In future, Amazon intends to participate in the research collaboration initiated by the Max Planck Society in December 2016 which is one of the biggest projects in the field of artificial intelligence (AI) in Europe. In addition to its commitment to Cyber Valley, Amazon is also setting up its own research center adjacent to the Max Planck Institute for Intelligent Systems in Tübingen and will step up collaboration with the Max Planck Society. “We appreciate Amazon’s commitment in the Cyber Valley and to research on artificial intelligence,” remarked Max Planck President Martin Stratmann. “We gain another strong cooperation partner who will further increase the international significance of research in the area of machine learning and computer vision in the Stuttgart and Tübingen region.”
Andreas GTC Europe talk on Deep Models for 3D Reconstruction is now available.
Robohub 2017 list of 25 women in Robotics you need to know about
On the occasion of Ada Lovelace Day on 10 October 2017, robohub presented their annual list of “25 women in robotics you need to know about”. Recently, Jeannette Bohg became Assistant Professor in Computer Science at Stanford. She is Guest Researcher at the Autonomous Motion Department of MPI, where she did her research on robotics between 2012 and 2017. Congratulations!
The Max Planck Institute for Intelligent Systems and the Universities of Stuttgart and Tübingen are collaborating to offer a new interdisciplinary Ph.D. program, the International Max Planck Research School for Intelligent Systems. This new doctoral program will be accepting its new generation of Ph.D. students in Spring 2018 and will enroll about 100 Ph.D. students over the next six years.
The Max Planck ETH Center for Learning Systems is a joint research center of ETH Zurich and the Max Planck Society. The Center’s mission is to pursue research in the design and analysis of learning systems, synthetic or natural. This initiative brings together more than 40 professors and senior researchers in the fields of machine learning, perception, robotics on large and small scales, as well as neuroscience.
Max Planck Lecture 2017
The field of transportation is undergoing a seismic change with the coming introduction of autonomous driving. The technologies required to enable computer driven cars involves the latest cutting edge artificial intelligence algorithms along three major thrusts: Sensing, Planning and Mapping.
The German Pattern Recognition Award is awarded once a year to one young researcher in computer vision, pattern recognition or machine learning at an age of 35 years or less and sponsored by the Daimler AG with 5000€.
3D reconstruction from multiple 2D images is an inherently ill-posed problem. Prior knowledge is required to resolve ambiguities and probabilistic models are desirable to capture the ambiguities in the reconstructed model. In this talk, I will present two recent results tackling these two aspects. First, I will introduce a probabilistic framework for volumetric 3D reconstruction where the reconstruction problem is cast as inference in a Markov random field using ray potentials. Our main contribution is a discrete-continuous inference algorithm which computes marginal distributions of each voxel's occupancy and appearance. I will show that the proposed algorithm allows for Bayes optimal predictions with respect to a natural reconstruction loss. I will further demonstrate several extensions which integrate non-local CAD priors into the reconstruction process. In the second part of my talk, I will present a novel framework for deep learning with 3D data called OctNet which enables 3D CNNs on high-dimensional inputs. I will demonstrate the utility of the OctNet representation on several 3D tasks including classification, orientation estimation and point cloud labeling. Finally, I will present an extension of OctNet called OctNetFusion which jointly predicts the space partitioning function with the output representation, resulting in an end-to-end trainable model for volumetric depth map fusion.