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Deep compositional robotic planners that follow natural language commands. (2020). CBMM-Memo-124.pdf (1.03 MB)
Encoding formulas as deep networks: Reinforcement learning for zero-shot execution of LTL formulas. (2020). CBMM-Memo-125.pdf (2.12 MB)
Encoding formulas as deep networks: Reinforcement learning for zero-shot execution of LTL formulas. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2020). doi:10.1109/IROS45743.2020.9341325
Learning a Natural-language to LTL Executable Semantic Parser for Grounded Robotics. (Proceedings of Conference on Robot Learning (CoRL-2020), 2020). at <https://corlconf.github.io/paper_385/>
Learning a natural-language to LTL executable semantic parser for grounded robotics. (2020). doi:https://doi.org/10.48550/arXiv.2008.03277 CBMM-Memo-122.pdf (1.03 MB)
PHASE: PHysically-grounded Abstract Social Eventsfor Machine Social Perception. Shared Visual Representations in Human and Machine Intelligence (SVRHM) workshop at NeurIPS 2020 (2020). at <https://openreview.net/forum?id=_bokm801zhx> phase_physically_grounded_abstract_social_events_for_machine_social_perception.pdf (2.49 MB)
Deep Compositional Robotic Planners that Follow Natural Language Commands. Workshop on Visually Grounded Interaction and Language (ViGIL) at the Thirty-third Annual Conference on Neural Information Processing Systems (NeurIPS), (2019). at <https://vigilworkshop.github.io/>
Deep video-to-video transformations for accessibility with an application to photosensitivity. Pattern Recognition Letters (2019). doi:10.1016/j.patrec.2019.01.019
How Does the Brain Represents Language and Answers Questions? Using an AI System to Understand the Underlying Neurobiological Mechanisms. Frontiers in Computational Neuroscience 13, (2019).
Learning Language from Vision. Workshop on Visually Grounded Interaction and Language (ViGIL) at the Thirty-third Annual Conference on Neural Information Processing Systems (NeurIPS) (2019).
ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models. Neural Information Processing Systems (NeurIPS 2019) (2019). 9142-objectnet-a-large-scale-bias-controlled-dataset-for-pushing-the-limits-of-object-recognition-models.pdf (16.31 MB)
Assessing Language Proficiency from Eye Movements in Reading. 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (2018). at <http://naacl2018.org/> 1804.07329.pdf (350.43 KB)
Deep sequential models for sampling-based planning. The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018) (2018). doi:10.1109/IROS.2018.8593947 kuo2018planning.pdf (637.67 KB)
Grounding language acquisition by training semantic parsersusing captioned videos. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP 2018), (2018). at <http://aclweb.org/anthology/D18-1285> Ross-et-al_ACL2018_Grounding language acquisition by training semantic parsing using caption videos.pdf (3.5 MB)
The Wiley Handbook of Human Computer Interaction 2, 539-559 (John Wiley & Sons , 2018).
Partially Occluded Hands: A challenging new dataset for single-image hand pose estimation. (2018). CBMM-Memo-097.pdf (8.53 MB)
Partially Occluded Hands: A challenging new dataset for single-image hand pose estimation. The 14th Asian Conference on Computer Vision (ACCV 2018) (2018). at <http://accv2018.net/> partially-occluded-hands-6.pdf (8.29 MB)
Predicting Native Language from Gaze. Annual Meeting of the Association for Computational Linguistics (ACL 2017) (2017).
Temporal Grounding Graphs for Language Understanding with Accrued Visual-Linguistic Context. Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI 2017) (2017). at <c>
Anchoring and Agreement in Syntactic Annotations. (2016). CBMM-Memo-055.pdf (768.54 KB)
Contrastive Analysis with Predictive Power: Typology Driven Estimation of Grammatical Error Distributions in ESL. (2016). memo-50.pdf (493.74 KB)
Do You See What I Mean? Visual Resolution of Linguistic Ambiguities. (2016). memo-51.pdf (2.74 MB)
Language and Vision Ambiguities (LAVA) Corpus. (2016). at <http://web.mit.edu/lavacorpus/> D15-1172.pdf (2.42 MB)
Learning to Answer Questions from Wikipedia Infoboxes. The 2016 Conference on Empirical Methods on Natural Language Processing (EMNLP 2016) (2016). Morales-EMNLP2016.pdf (197.28 KB)
A look back at the June 2016 BMM Workshop in Sestri Levante, Italy. (2016). Sestri Levante Review (359.33 KB)