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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2020

Netanyahu, A., Shu, T., Katz, B., Barbu, A. & Tenenbaum, J. B. 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)

2021

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)

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).

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)

Yaari, A. Uri et al. 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>

Conwell, C. et al. Neural Regression, Representational Similarity, Model Zoology Neural Taskonomy at Scale in Rodent Visual Cortex. (2021).

CBMM-Memo-131.pdf (9.37 MB)

Netanyahu, A., Shu, T., Katz, B., Barbu, A. & Tenenbaum, J. B. PHASE: PHysically-grounded Abstract Social Events for Machine Social Perception. (2021).

CBMM-Memo-123.pdf (3.08 MB)

Netanyahu, A., Shu, T., Katz, B., Barbu, A. & Tenenbaum, J. B. PHASE: PHysically-grounded Abstract Social Events for Machine Social Perception. AAAI-21 (2021).

Tejwani, R., Kuo, Y. - L., Shu, T., Katz, B. & Barbu, A. Social Interactions as Recursive MDPs. (2021).

CBMM-Memo-130.pdf (1.52 MB)

Palmer, I., Rouditchenko, A., Barbu, A., Katz, B. & Glass, J. Spoken ObjectNet: A Bias-Controlled Spoken Caption Dataset. (2021).

CBMM-Memo-128.pdf (2.91 MB)

2022

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).

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

CBMM-Memo-133.pdf (1.68 MB)

Cheng, E. et al. Quantifying the Emergence of Symbolic Communication. CogSci (2022). at <https://escholarship.org/uc/item/08n3293v>

Cheng, E., Kuo, Y. - L., Cases, I., Katz, B. & Barbu, A. Spontaneous sign emergence in humans and machines through an embodied communication game. JCoLE Workshop (2022).

Kuo, Y. - L. et al. Trajectory Prediction with Linguistic Representations. (2022).

CBMM-Memo-132.pdf (1.15 MB)

2023

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)

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>

Subramaniam, V. et al. Using Multimodal DNNs to Study Vision-Language Integration in the Brain. ICLR 2023 (2023). at <https://openreview.net/pdf?id=OQQ1p0pFP4>

Tejwani, R. et al. Zero-shot linear combinations of grounded social interactions with Linear Social MDPs. Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI) (2023).

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