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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 Hits 1 – 20 of 26 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 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 3 Trajectory Prediction with Linguistic Representations ... Kuo, Yen-Ling; Huang, Xin; Barbu, Andrei. - : arXiv, 2021 BASE Show details 4 Measuring Social Biases in Grounded Vision and Language Embeddings ... NAACL 2021 2021; Barbu, Andrei; Katz, Boris. - : Underline Science Inc., 2021 BASE Show details 5 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 6 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 Abstract: We generalize the notion of measuring social biases in word embeddings to visually grounded word embeddings. Biases are present in grounded embeddings, and indeed seem to be equally or more significant than for ungrounded embeddings. This is despite the fact that vision and language can suffer from different biases, which one might hope could attenuate the biases in both. Multiple ways exist to generalize metrics measuring bias in word embeddings to this new setting. We introduce the space of generalizations (GroundedWEAT and Grounded-SEAT) and demonstrate that three gener- alizations answer different yet important questions about how biases, language, and vision interact. These metrics are used on a new dataset, the first for grounded bias, created by augmenting stan- dard linguistic bias benchmarks with 10,228 images from COCO, Conceptual Captions, and Google Images. Dataset construction is challenging because vision datasets are themselves very biased. The presence of these biases in systems will begin to have real-world consequences as they are deployed, making carefully measuring bias and then mitigating it critical to building a fair society. ; This material is based upon work supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF-1231216. URL: https://hdl.handle.net/1721.1/141356 BASE Hide details 7 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 8 Compositional Networks Enable Systematic Generalization for Grounded Language Understanding ... Kuo, Yen-Ling; Katz, Boris; Barbu, Andrei. - : arXiv, 2020 BASE Show details 9 Learning a natural-language to LTL executable semantic parser for grounded robotics ... Wang, Christopher; Ross, Candace; Kuo, Yen-Ling. - : arXiv, 2020 BASE Show details 10 Measuring Social Biases in Grounded Vision and Language Embeddings ... Ross, Candace; Katz, Boris; Barbu, Andrei. - : arXiv, 2020 BASE Show details 11 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 12 Temporal Grounding Graphs for Language Understanding with Accrued Visual-Linguistic Context ... Paul, Rohan; Barbu, Andrei; Felshin, Sue. - : arXiv, 2018 BASE Show details 13 Temporal Grounding Graphs for Language Understanding with Accrued Visual-Linguistic Context Paul, Rohan; Barbu, Andrei; Felshin, Sue... In: Other repository (2018) BASE Show details 14 Do You See What I Mean? Visual Resolution of Linguistic Ambiguities Berzak, Yevgeni; Barbu, Andrei; Harari, Daniel. - : Center for Brains, Minds and Machines (CBMM), arXiv, 2016 BASE Show details 15 Anchoring and Agreement in Syntactic Annotations Berzak, Yevgeni; Huang, Yan; Barbu, Andrei. - : Center for Brains, Minds and Machines (CBMM), arXiv, 2016 BASE Show details 16 Do You See What I Mean? Visual Resolution of Linguistic Ambiguities ... Berzak, Yevgeni; Barbu, Andrei; Harari, Daniel. - : arXiv, 2016 BASE Show details 17 Seeing What You’re Told: Sentence-Guided Activity Recognition In Video Siddharth, Narayanaswamy; Barbu, Andrei; Siskind, Jeffrey Mark. - : Center for Brains, Minds and Machines (CBMM), arXiv, 2014 BASE Show details 18 The Compositional Nature of Event Representations in the Human Brain Barbu, Andrei; Narayanaswamy, Siddharth; Xiong, Caiming. - : Center for Brains, Minds and Machines (CBMM), arXiv, 2014 BASE Show details 19 Video In Sentences Out ... Barbu, Andrei; Bridge, Alexander; Burchill, Zachary. - : arXiv, 2014 BASE Show details 20 Saying What You're Looking For: Linguistics Meets Video Search ... Barbu, Andrei; Siddharth, N.; Siskind, Jeffrey Mark. - : arXiv, 2013 BASE Show details Page: 1 2

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