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Prior parameter distributions provide an elegant way to represent prior expert and world knowledge for informed learning.
Random features for large-scale kernel machines
Ali Rahimi and Benjamin Recht · 2007
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Introduction to Algorithms, 3rd Edition
Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein · 2009
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Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A. Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, and Raia Hadsell · 2017
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Progress & compress: A scalable framework for continual learning
Jonathan Schwarz, Wojciech Czarnecki, Jelena Luketina, Agnieszka Grabska-Barwinska, Yee Whye Teh, Razvan Pascanu, and Raia Hadsell · 2018
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Continual lifelong learning with neural networks: A review
German Ignacio Parisi, Ronald Kemker, Jose L. Part, Christopher Kanan, and Stefan Wermter · 2019
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nuscenes: A multimodal dataset for autonomous driving
Holger Caesar, Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom · 2020
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Deep kinematic models for kinematically feasible vehicle trajectory predictions
Henggang Cui, Thi Nguyen, Fang-Chieh Chou, Tsung-Han Lin, Jeff Schneider, David Bradley, and Nemanja Djuric · 2020
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Being bayesian, even just a bit, fixes overconfidence in relu networks
Agustinus Kristiadi, Matthias Hein, and Philipp Hennig · 2020
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Simple and principled uncertainty estimation with deterministic deep learning via distance awareness
Jeremiah Z. Liu, Zi Lin, Shreyas Padhy, Dustin Tran, Tania Bedrax-Weiss, and Balaji Lakshminarayanan · 2020
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Covernet: Multimodal behavior prediction using trajectory sets
Tung Phan-Minh, Elena Corina Grigore, Freddy A. Boulton, Oscar Beijbom, and Eric M. Wolff · 2020
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Functional regularisation for continual learning with gaussian processes
Michalis K. Titsias, Jonathan Schwarz, Alexander G. de G. Matthews, Razvan Pascanu, and Yee Whye Teh · 2020
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An investigation of replay-based approaches for continual learning
Benedikt Bagus and Alexander Gepperth · 2021
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Injecting Knowledge in Data-driven Vehicle Trajectory Predictors
Mohammadhossein Bahari, Ismail Nejjar, and Alexandre Alahi · 2021
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Motion Prediction using Trajectory Sets and Self-Driving Domain Knowledge
Freddy A. Boulton, Elena Corina Grigore, and Eric M. Wolff · 2021
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Kernel continual learning
Mohammad Mahdi Derakhshani, Xiantong Zhen, Ling Shao, and Cees Snoek · 2021
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Generalized variational continual learning
A Survey on Trajectory-Prediction Methods for Autonomous Driving
Yanjun Huang, Jiatong Du, Ziru Yang, Zewei Zhou, Lin Zhang, and Hong Chen · 2022
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You mostly walk alone: Analyzing feature attribution in trajectory prediction
Osama Makansi, Julius von Kügelgen, Francesco Locatello, Peter Vincent Gehler, Dominik Janzing, Thomas Brox, and Bernhard Schölkopf · 2022
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On the practicality of deterministic epistemic uncertainty
Janis Postels, Mattia Segù, Tao Sun, Luca Daniel Sieber, Luc Van Gool, Fisher Yu, and Federico Tombari · 2022
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Pre-train your loss: Easy bayesian transfer learning with informative priors
Ravid Shwartz-Ziv, Micah Goldblum, Hossein Souri, Sanyam Kapoor, Chen Zhu, Yann LeCun, and Andrew Gordon Wilson · 2022
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Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey
Julian Wörmann, Daniel Bogdoll, Etienne Bührle, Han Chen, Evaristus Fuh Chuo, Kostadin Cvejoski, Ludger van Elst, Tobias Gleißner, Philip Gottschall, Stefan Griesche, Christian Hellert, Christian Hesels, Sebastian Houben, Tim Joseph, Niklas Keil, Johann Kelsch, Hendrik Königshof, Erwin Kraft, Leonie Kreuser, Kevin Krone, Tobias Latka, Denny Mattern, Stefan Matthes, Mohsin Munir, Moritz Nekolla, Adrian Paschke, Maximilian Alexander Pintz, Tianming Qiu, Faraz Qureishi, Syed Tahseen Raza Rizvi, Jörg Reichardt, Laura von Rueden, Stefan Rudolph, Alexander Sagel, Gerhard Schunk, Hao Shen, Hendrik Stapelbroek, Vera Stehr, Gurucharan Srinivas, Anh Tuan Tran, Abhishek Vivekanandan, Ya Wang, Florian Wasserrab, Tino Werner, Christian Wirth, and Stefan Zwicklbauer · 2022
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Noel Loo, Siddharth Swaroop, and Richard E. Turner · 2021
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Shifts: A dataset of real distributional shift across multiple large-scale tasks
Andrey Malinin, Neil Band, Yarin Gal, Mark J. F. Gales, Alexander Ganshin, German Chesnokov, Alexey Noskov, Andrey Ploskonosov, Liudmila Prokhorenkova, Ivan Provilkov, Vatsal Raina, Vyas Raina, Denis Roginskiy, Mariya Shmatova, Panagiotis Tigas, and Boris Yangel · 2021
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On feature collapse and deep kernel learning for single forward pass uncertainty
Joost R. van Amersfoort, Lewis Smith, Andrew Jesson, Oscar Key, and Yarin Gal · 2021
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Informed Machine Learning – A Taxonomy and Survey of Integrating Knowledge into Learning Systems
Laura von Rueden, Sebastian Mayer, Katharina Beckh, Bogdan Georgiev, Sven Giesselbach, Raoul Heese, Birgit Kirsch, Julius Pfrommer, Annika Pick, Rajkumar Ramamurthy, Michal Walczak, Jochen Garcke, Christian Bauckhage, and Jannis Schuecker · 2021
Cited alongside, same era.
A continual learning survey: Defying forgetting in classification tasks
Matthias De Lange, Rahaf Aljundi, Marc Masana, Sarah Parisot, Xu Jia, Ales Leonardis, Gregory G. Slabaugh, and Tinne Tuytelaars · 2022
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Training, architecture, and prior for deterministic uncertainty methods
Bertrand Charpentier, Chenxiang Zhang, and Stephan Günnemann · 2023
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Beyond generalization: a theory of robustness in machine learning
Timo Freiesleben and Thomas Grote · 2023
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Informed priors for knowledge integration in trajectory prediction
Christian Schlauch, Christian Wirth, and Nadja Klein · 2023
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Argoverse 2: Next generation datasets for self-driving perception and forecasting
Benjamin Wilson, William Qi, Tanmay Agarwal, John Lambert, Jagjeet Singh, Siddhesh Khandelwal, Bowen Pan, Ratnesh Kumar, Andrew Hartnett, Jhony Kaesemodel Pontes, Deva Ramanan, Peter Carr, and James Hays · 2023
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