2nd workshop on Unsupervised Learning for Automated Driving
Half-day workshop at the IEEE Intelligent Vehicles Symposium 2020, October 20, 2020, Las Vegas, NV, USA
supported by the IEEE ITS Technical Committee Self Driving Automobiles
Unlabelled data is easily collected, increasing traction in IV to explore unsupervised learning, its semi-, weakly-, and self-supervised variants, transfer learning, and inferring probabilistic latent representations. Share novel methodological developments, challenges and solutions for exploiting unlabelled data and reducing the annotation bottleneck.
While the schedule is still tentative, we are already very pleased to announce the following keynote speakers with diverse academic and industrial backgrounds, see Keynote details below for more information.
Note: all times in Pacific Time (PT)
- 09:00-09:15: Introduction WS organizers
- 09:15-10:00: 1st Keynote: Anima Anandkumar (Professor at Caltech, director of Machine Learning at NIVIDIA)
- 10:00-10:45: 2nd Keynote: Zsolt Kira (Assistant professor at Georgia Tech)
- 10:45-11:00: Break
- 11:00-11:45: 3rd Keynote: Tudor Achim (CTO Helm.ai)
- 11:45-12:30: 4th Keynote: To be decided
- 12:30-13:30: Round-table discussion with all speakers
Machine learning is nowadays omnipresent in automated driving, and applied on sensor data for perception, context cues for intent recognition, trajectories for path prediction and planning, scenario clustering for test specification, density estimation for anomalous event detection, and realistic data generation for simulation. Large annotated datasets and benchmarks are key to advancing the state-of-the-art, and have so far mainly involved supervised learning.
However, the supervised learning paradigm has its limitations, since (a) new sensors or environment conditions require new labelling to counter domain shift, (b) creating manual annotations is labour intensive and time consuming, (c) some properties may be infeasible to collect (e.g. rare events) or annotate (e.g. mental states), (d) sometimes the relevant structure in the data is unknown and must be discovered (e.g. clustering, dimensionality reduction).
On the other hand, unlabelled data is easily collected, increasing traction in IV to explore unsupervised learning, its semi-, weakly-, and self-supervised variants, transfer learning, and inferring probabilistic latent representations.
This workshop therefore explores the varied use of machine learning techniques on unlabelled, partially or automatically labelled data throughout all IV research domains, encouraging IV researchers to share novel developments, challenges and solutions.
Anima Anandkumar holds dual positions in academia and industry. She is a Bren professor at Caltech CMS department and a director of machine learning research at NVIDIA. At NVIDIA, she is leading the research group that develops next-generation AI algorithms. At Caltech, she is the co-director of Dolcit and co-leads the AI4science initiative, along with Yisong Yue.
She has spearheaded the development of tensor algorithms, first proposed in her seminal paper. They are central to effectively processing multidimensional and multimodal data, and for achieving massive parallelism in large-scale AI applications.
Prof. Anandkumar is the youngest named chair professor at Caltech, the highest honor the university bestows on individual faculty. She is recipient of several awards such as the Alfred. P. Sloan Fellowship, NSF Career Award, Faculty fellowships from Microsoft, Google and Adobe, and Young Investigator Awards from the Army research office and Air Force office of sponsored research. She has been featured in documentaries and articles by PBS, wired magazine, MIT Technology review, yourstory, and Forbes.
Anima received her B.Tech in Electrical Engineering from IIT Madras in 2004 and her PhD from Cornell University in 2009. She was a postdoctoral researcher at MIT from 2009 to 2010, visiting researcher at Microsoft Research New England in 2012 and 2014, assistant professor at U.C. Irvine between 2010 and 2016, associate professor at U.C. Irvine between 2016 and 2017, and principal scientist at Amazon Web Services between 2016 and 2018.
Dr. Zsolt Kira is an Assistant Professor at the Georgia Institute of Technology, branch chief of the Machine Learning and Analytics group at the Georgia Tech Research Institute (GTRI), and Associate Director of Georgia Tech’s Machine Learning Center. His work lies at the intersection of machine learning and artificial intelligence for sensor processing, perception, and robotics, emphasizing the fusion of multiple sources of information for scene understanding. Current projects and interests relate to moving beyond current limitations of machine learning to tackle unsupervised/semi-supervised methods, continual/lifelong learning, and adaptation as well as distributed perception across heterogeneous robot/sensor teams. Dr. Kira has grown a portfolio of projects funded by NSF, ONR, DARPA, and the IC community. He also has won several best paper/student paper awards, taught several graduate and undergraduate machine/deep learning courses at Georgia Tech, and been invited to speak at related workshops in both academia and the within the DoD.
Tudor Achim is a computer scientist with a decade of experience in artificial intelligence and software engineering. At 19 years old, Tudor graduated from Carnegie Mellon University with a BA in Computer Science and a minor in Mathematics. He helped start the machine learning team at Quora at age 20 where he led machine learning-based ranking improvements. Prior to founding Helm.ai, Tudor spent two years researching theoretical Machine Learning in the Computer Science PhD program at Stanford, during which he published papers in NeurIPS, ICML, and AISTATS. Helm.ai is an automotive software company that is building and productizing cutting edge artificial intelligence technology which unlocks the full market potential for fully autonomous driving. Our primary focus is on developing flexible deep learning based automotive-grade software solutions throughout the entire self-driving stack, including computer vision and sensor fusion based perception, intent modeling, path planning, and control, all with the levels of reliability required for large-scale full autonomy.
- ULAD 2019 at IEEE Intelligent Vehicles Symposium 2019
- Slides of ULAD 2019 presentations can be found on the website!
Topics of interest
- unsupervised, semi-supervised, and weakly-supervised learning
- self-supervised learning
- domain adaptation
- GANs & VAEs
- missing data
- representation learning
- density estimation
- anomaly detection
- clustering and data analysis
- generative graphical models
- latent variable models
- non-parametric models
- artificial data generation and simulation
- IEEE Intelligent Vehicles Symposium 2020
June 23-26, 2020, Las Vegas, NV, United States
- October 20-23, 2020, Las Vegas, NV, United States
(new dates due to COVID-19)
Information for authors
- Authors of accepted workshop papers will be published in the conference proceeding. Papers will go through the same peer-review process as regular conference submissions.
- At least one author needs to be registered for the workshop and the conference.
- Information on paper and submissions available at https://2020.ieee-iv.org/information-for-authors/
for questions, please send us a mail at info[at]ulad-workshop.com