Publications

CBMM Memos were established in 2014 as a mechanism for our center to share research results with the wider scientific community. Click here to read more about the memos and to see a full list of the memos.

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Found 46 results

Filters: Author is Boris Katz [Clear All Filters]

A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

A

Yaari, A. et al. The Aligned Multimodal Movie Treebank: An audio, video, dependency-parse treebank. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (2022).

Berzak, Y., Huang, Y., Barbu, A., Korhonen, A. & Katz, B. Anchoring and Agreement in Syntactic Annotations. (2016).

CBMM-Memo-055.pdf (768.54 KB)

Berzak, Y., Katz, B. & Levy, R. 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)

B

Wang, C. et al. BrainBERT: Self-supervised representation learning for Intracranial Electrodes. International Conference on Learning Representations (2023). at <https://openreview.net/forum?id=xmcYx_reUn6>

985_brainbert_self_supervised_repr.pdf (9.71 MB)

C

Kuo, Y. - L., Katz, B. & Barbu, A. Compositional Networks Enable Systematic Generalization for Grounded Language Understanding. (2021).

CBMM-Memo-129.pdf (1.2 MB)

Kuo, Y. - L., Barbu, A. & Katz, B. Compositional RL Agents That Follow Language Commands in Temporal Logic. (2021).

CBMM-Memo-127.pdf (2.12 MB)

Berzak, Y., Reichart, R. & Katz, B. Contrastive Analysis with Predictive Power: Typology Driven Estimation of Grammatical Error Distributions in ESL. Nineteenth Conference on Computational Natural Language Learning (CoNLL), Beijing, China (2015).

Berzak, Y., Reichart, R. & Katz, B. Contrastive Analysis with Predictive Power: Typology Driven Estimation of Grammatical Error Distributions in ESL. (2016).

memo-50.pdf (493.74 KB)

D

Kuo, Y. - L., Katz, B. & Barbu, A. Deep compositional robotic planners that follow natural language commands. (2020).

CBMM-Memo-124.pdf (1.03 MB)

Kuo, Y. - L., Barbu, A. & Katz, B. 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)

Berzak, Y., Barbu, A., Harari, D., Katz, B. & Ullman, S. Do You See What I Mean? Visual Resolution of Linguistic Ambiguities. Conference on Empirical Methods in Natural Language Processing, Lisbon, Portugal. (2015).

Berzak, Y., Barbu, A., Harari, D., Katz, B. & Ullman, S. Do You See What I Mean? Visual Resolution of Linguistic Ambiguities. (2016).

memo-51.pdf (2.74 MB)

E

Kuo, Y. - L., Katz, B. & Barbu, A. Encoding formulas as deep networks: Reinforcement learning for zero-shot execution of LTL formulas. (2020).

CBMM-Memo-125.pdf (2.12 MB)

G

Ross, C., Barbu, A., Berzak, Y., Myanganbayar, B. & Katz, B. 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)

I

Tejwani, R. et al. Incorporating Rich Social Interactions Into MDPs. (2022).

CBMM-Memo-133.pdf (1.68 MB)

L

Berzak, Y., Barbu, A., Harari, D., Katz, B. & Ullman, S. Language and Vision Ambiguities (LAVA) Corpus. (2016). at <http://web.mit.edu/lavacorpus/>

D15-1172.pdf (2.42 MB)

Conwell, C. et al. Large-scale benchmarking of deep neural network models in mouse visual cortex reveals patterns similar to those observed in macaque visual cortex. Cosyne (2021).

Wang, C., Ross, C., Kuo, Y. - L., Katz, B. & Barbu, A. 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/>

Wang, C., Ross, C., Kuo, Y. - L., Katz, B. & Barbu, A. 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)

Ross, C., Berzak, Y., Katz, B. & Barbu, A. 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).

Morales, A., Premtoon, V., Avery, C., Felshin, S. & Katz, B. 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)

Katz, B. & Barbu, A. A look back at the June 2016 BMM Workshop in Sestri Levante, Italy. (2016).

Sestri Levante Review (359.33 KB)

M

Ross, C., Katz, B. & Barbu, A. Measuring Social Biases in Grounded Vision and Language Embeddings. NAACL (Annual Conference of the North American Chapter of the Association for Computational Linguistics) (2021).

Ross, C., Barbu, A. & Katz, B. Measuring Social Biases in Grounded Vision and Language Embeddings. (2021).

CBMM-Memo-126.pdf (1.32 MB)

Schiatti, L. et al. Modeling Visual Impairments with Artificial Neural Networks: a Review. International Conference on Computer Vision 2023 (2023). at <https://openaccess.thecvf.com/content/ICCV2023W/ACVR/html/Schiatti_Modeling_Visual_Impairments_with_Artificial_Neural_Networks_a_Review_ICCVW_2023_paper.html>

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