The Quantum Computing Survival Guide
Abstract: This talk explains the fundamental mathematics of gate based Quantum Computing. It will cover three topics: single Qubit systems, multi Qubit systems, and an outlook on applications and Quantum Machine Learning. Concepts will be demonstrated using Pennylane, a Python library for Quantum Circuit simulation. This talk is intended for an audience with no prior knowledge in Quantum Computing/Quantum Physics.
Brief Bio: Cameron Braunstein is a Master’s student at Saarland University studying data science and artificial intelligence. He received his Bachelor’s degree in mathematics and computer science from Brandeis University in 2019. He is interested in computer vision, and in integrating quantum techniques into classical machine learning models. His thesis, cosupervised by Prof. Eddy Ilg from the CVMP lab and Vladislav Golyanik from MPI, explores quantum computing approaches to optical flow. This work not only aims to improve performance speed and accuracy of optical flow, but also to gain more general insights into where quantum techniques succeed and where they require assistance from classical models.
3D Representations for Deep Learning in Computer Vision
Title: 3D Representations for Deep Learning in Computer Vision
Presenter: Prof. Dr.-Ing. Eddy Ilg
Location: E 1.4, Room 024
Time: Friday, August 19th at 2pm
Meeting ID: 970 4487 3513
In the recent history of computer vision, methods leveraging deep learning and 3D representations have shown great success. The lecture will give an introduction to 3D computer vision, starting with the fundamentals of 3D reconstruction and providing an overview of state-of-the-art 3D scene and object representations with point clouds, surface- and density based approaches. The lecture will conclude with an outlook on the future direction of the field and an overview of the research of the new CVMP lab at Saarland University.
Eddy Ilg is a new professor at Saarland University and leads the Computer Vision and Machine Perception (CVMP) lab. He received his PhD from the University of Freiburg and is author of FlowNet, FlowNet 2.0 and FlowNetH, which were the first in their field. After his PhD, Eddy has spent three years at Facebook (now Meta) Reality Labs, focusing on 3D object reconstruction in the wild. He is now starting a new research group at Saarland University that will focus on 3D reconstruction in the combination with continual learning, with the goal of building future machine perception algorithms that are not rigid but able to adapt to their environment and evolve.