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Finding the Optimal Network Depth in Classification Tasks.

, , , and . ECML/PKDD (3), volume 12459 of Lecture Notes in Computer Science, page 263-278. Springer, (2020)

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Disentangling Transfer in Continual Reinforcement Learning., , , , and . NeurIPS, (2022)Urban Driver: Learning to Drive from Real-world Demonstrations Using Policy Gradients., , , , and . CoRL, volume 164 of Proceedings of Machine Learning Research, page 718-728. PMLR, (2021)Remember More by Recalling Less: Investigating the Role of Batch Size in Continual Learning with Experience Replay (Student Abstract)., and . AAAI, page 15923-15924. AAAI Press, (2021)Hebbian Continual Representation Learning., , , and . HICSS, page 1259-1268. ScholarSpace, (2023)Finding the Optimal Network Depth in Classification Tasks., , , and . ECML/PKDD (3), volume 12459 of Lecture Notes in Computer Science, page 263-278. Springer, (2020)On the Relationship Between Disentanglement and Multi-task Learning., , , and . ECML/PKDD (1), volume 13713 of Lecture Notes in Computer Science, page 625-641. Springer, (2022)The Effectiveness of World Models for Continual Reinforcement Learning., , , , , , , and . CoLLAs, volume 232 of Proceedings of Machine Learning Research, page 184-204. PMLR, (2023)Hebbian Continual Representation Learning., , , and . CoRR, (2022)SafetyNet: Safe Planning for Real-World Self-Driving Vehicles Using Machine-Learned Policies., , , , , , , , , and 1 other author(s). ICRA, page 897-904. IEEE, (2022)Zero Time Waste: Recycling Predictions in Early Exit Neural Networks., , , , , , and . NeurIPS, page 2516-2528. (2021)