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Incorporating Rich Social Interactions Into MDPs. (2022).
CBMM-Memo-133.pdf (1.68 MB)

Trajectory Prediction with Linguistic Representations. (2022).
CBMM-Memo-132.pdf (1.15 MB)

Compositional Networks Enable Systematic Generalization for Grounded Language Understanding. (2021).
CBMM-Memo-129.pdf (1.2 MB)

Compositional RL Agents That Follow Language Commands in Temporal Logic. Frontiers in Robotics and AI 8, (2021).
frobt-08-689550.pdf (1.57 MB)

Compositional RL Agents That Follow Language Commands in Temporal Logic. (2021).
CBMM-Memo-127.pdf (2.12 MB)

Measuring Social Biases in Grounded Vision and Language Embeddings. (2021).
CBMM-Memo-126.pdf (1.32 MB)

Measuring Social Biases in Grounded Vision and Language Embeddings. NAACL (Annual Conference of the North American Chapter of the Association for Computational Linguistics) (2021).
Multi-resolution modeling of a discrete stochastic process identifies causes of cancer. International Conference on Learning Representations (2021). at <https://openreview.net/forum?id=KtH8W3S_RE>
Neural Regression, Representational Similarity, Model Zoology Neural Taskonomy at Scale in Rodent Visual Cortex. (2021).
CBMM-Memo-131.pdf (9.37 MB)

PHASE: PHysically-grounded Abstract Social Events for Machine Social Perception. (2021).
CBMM-Memo-123.pdf (3.08 MB)

Social Interactions as Recursive MDPs. (2021).
CBMM-Memo-130.pdf (1.52 MB)

Spoken ObjectNet: A Bias-Controlled Spoken Caption Dataset. Interspeech 2021 (2021). doi:10.21437/Interspeech.2021
Spoken ObjectNet: A Bias-Controlled Spoken Caption Dataset. (2021).
CBMM-Memo-128.pdf (2.91 MB)

Deep compositional robotic planners that follow natural language commands. (2020).
CBMM-Memo-124.pdf (1.03 MB)

Deep compositional robotic planners that follow natural language commands . International Conference on Robotics and Automation (ICRA) (2020).
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).