About:
Black in AI is a place for sharing ideas, fostering collaborations and discussing initiatives to increase the presence of Black people in the field of Artificial Intelligence. The workshop will feature invited talks from prominent researchers and practitioners, oral presentations, and a poster session. Uber is a sponsor.
About:
This technical workshop gives female faculty, research scientists, and graduate students in the machine learning community an opportunity to meet, network and exchange ideas, participate in career-focused panel discussions with senior women in industry and academia and learn from each other. Uber is a Silver Sponsor.
About:
Statistical learning theory focuses on the complexity of the hypothesis class to bound the generalization gap, but it is clear that this approach won’t work for deep networks. How can we adapt existing theory to exploit the geometry of the hypothesis class? The learning algorithm can be viewed as an information processing procedure. What information-theoretic properties of this channel lead to good generalization?
How should we build an understanding of data in machine learning? Specifically, how does the dataset (task) in deep learning affect optimization and generalization? How can we adapt learning theory to understand the low-data regime?
Accepted Paper:
Annealed importance sampling with q-paths
Rob Brekelmans, Vaden Masrani, Thang D Bui, Frank Wood, Aram Galstyan, Greg Ver Steeg, Frank Nielsen
About:
This workshop aims to bring together researchers from all the different communities and topics that fall under the umbrella of meta-learning. We expect that the presence of these different communities will result in a fruitful exchange of ideas and stimulate an open discussion about the current challenges in meta-learning as well as possible solutions.
Accepted Paper:
Flexible Few-Shot Learning of Contextual Similarity
Mengye Ren, Eleni Triantafillou, Kuan-Chieh Wang, James Lucas, Jake Snell, Xaq Pitkow, Andreas Tolias, Richard Zemel
About:
Autonomous vehicles (AVs) offer a rich source of high-impact research problems for the machine learning (ML) community; including perception, state estimation, probabilistic modeling, time series forecasting, gesture recognition, robustness guarantees, real-time constraints, user-machine communication, multi-agent planning, and intelligent infrastructure. Further, the interaction between ML subfields towards a common goal of autonomous driving can catalyze interesting inter-field discussions that spark new avenues of research, which this workshop aims to promote. As an application of ML, autonomous driving has the potential to greatly improve society by reducing road accidents, giving independence to those unable to drive, and even inspiring younger generations with tangible examples of ML-based technology clearly visible on local streets.
Program Committee: Henggang Cui, Nemanja Djuric
Accepted Papers:
Investigating the Effect of Sensor Modalities in Multi-Sensor Detection-Prediction Models
Abhishek Mohta, Fang-Chieh Chou, Brian Becker, Carlos Vallespi-Gonzalez, Nemanja Djuric
Temporally-Continuous Probabilistic Prediction using Polynomial Trajectory Parameterization
Zhaoen Su, Chao Wang, Henggang Cui, Nemanja Djuric, Carlos Vallespi-Gonzalez, David Bradley
Uncertainty-aware Vehicle Orientation Estimation for Joint Detection-Prediction Models
Henggang Cui, Fang-Chieh Chou, Jake Charland, Carlos Vallespi-Gonzalez, Nemanja Djuric
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