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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 3 Hits 1 – 20 of 48 1 Trajectory Prediction with Linguistic Representations Kuo, Yen-Ling; Huang, Xin; Barbu, Andrei. - : Center for Brains, Minds and Machines (CBMM), International Conference on Robotics and Automation (ICRA), 2022 BASE Show details 2 Universal Dependencies 2.9 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021 BASE Show details 3 Universal Dependencies 2.8.1 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021 BASE Show details 4 Universal Dependencies 2.8 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021 BASE Show details 5 Compositional Networks Enable Systematic Generalization for Grounded Language Understanding Kuo, Yen-Ling; Katz, Boris; Barbu, Andrei. - : Center for Brains, Minds and Machines (CBMM), Conference on Empirical Methods in Natural Language Processing (EMNLP), 2021 BASE Show details 6 Trajectory Prediction with Linguistic Representations ... Kuo, Yen-Ling; Huang, Xin; Barbu, Andrei; McGill, Stephen G.; Katz, Boris; Leonard, John J.; Rosman, Guy. - : arXiv, 2021 Abstract: Language allows humans to build mental models that interpret what is happening around them resulting in more accurate long-term predictions. We present a novel trajectory prediction model that uses linguistic intermediate representations to forecast trajectories, and is trained using trajectory samples with partially-annotated captions. The model learns the meaning of each of the words without direct per-word supervision. At inference time, it generates a linguistic description of trajectories which captures maneuvers and interactions over an extended time interval. This generated description is used to refine predictions of the trajectories of multiple agents. We train and validate our model on the Argoverse dataset, and demonstrate improved accuracy results in trajectory prediction. In addition, our model is more interpretable: it presents part of its reasoning in plain language as captions, which can aid model development and can aid in building confidence in the model before deploying it. ... : Accepted in ICRA 2022 ... Keyword: Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG; Robotics cs.RO URL: https://arxiv.org/abs/2110.09741 https://dx.doi.org/10.48550/arxiv.2110.09741 BASE Hide details 7 Measuring Social Biases in Grounded Vision and Language Embeddings ... NAACL 2021 2021; Barbu, Andrei; Katz, Boris. - : Underline Science Inc., 2021 BASE Show details 8 Compositional Networks Enable Systematic Generalization for Grounded Language Understanding ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Barbu, Andrei; Katz, Boris. - : Underline Science Inc., 2021 BASE Show details 9 Assessing Language Proficiency from Eye Movements in Reading Berzak, Yevgeni; Katz, Boris; Levy, Roger P In: Association for Computational Linguistics (2021) BASE Show details 10 Measuring Social Biases in Grounded Vision and Language Embeddings Ross, Candace; Barbu, Andrei; Katz, Boris. - : Center for Brains, Minds and Machines (CBMM), Annual Conference of the North American Chapter of the Association for Computational Linguistics (HLT/NAACL), 2021 BASE Show details 11 Universal Dependencies 2.7 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2020 BASE Show details 12 Universal Dependencies 2.6 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2020 BASE Show details 13 Learning a natural-language to LTL executable semantic parser for grounded robotics Wang, Christopher; Ross, Candace; Kuo, Yen-Ling. - : Center for Brains, Minds and Machines (CBMM), Conference on Robot Learning (CoRL), 2020 BASE Show details 14 Compositional Networks Enable Systematic Generalization for Grounded Language Understanding ... Kuo, Yen-Ling; Katz, Boris; Barbu, Andrei. - : arXiv, 2020 BASE Show details 15 Learning a natural-language to LTL executable semantic parser for grounded robotics ... Wang, Christopher; Ross, Candace; Kuo, Yen-Ling. - : arXiv, 2020 BASE Show details 16 Measuring Social Biases in Grounded Vision and Language Embeddings ... Ross, Candace; Katz, Boris; Barbu, Andrei. - : arXiv, 2020 BASE Show details 17 Encoding formulas as deep networks: Reinforcement learning for zero-shot execution of LTL formulas Kuo, Yen-Ling; Katz, Boris; Barbu, Andrei. - : Center for Brains, Minds and Machines (CBMM), The Ninth International Conference on Learning Representations (ICLR), 2020 BASE Show details 18 Universal Dependencies 2.5 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2019 BASE Show details 19 Universal Dependencies 2.4 Nivre, Joakim; Abrams, Mitchell; Agić, Željko. - : Universal Dependencies Consortium, 2019 BASE Show details 20 Universal Dependencies 2.3 Nivre, Joakim; Abrams, Mitchell; Agić, Željko. - : Universal Dependencies Consortium, 2018 BASE Show details Page: 1 2 3

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