Publication

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2021
Kuo, Y. - L., Katz, B. & Barbu, A. Compositional Networks Enable Systematic Generalization for Grounded Language Understanding. (2021).PDF icon 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).PDF icon 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).PDF icon 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).PDF icon 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. AAAI-21 (2021).
Netanyahu, A., Shu, T., Katz, B., Barbu, A. & Tenenbaum, J. B. PHASE: PHysically-grounded Abstract Social Events for Machine Social Perception. (2021).PDF icon CBMM-Memo-123.pdf (3.08 MB)
Tejwani, R., Kuo, Y. - L., Shu, T., Katz, B. & Barbu, A. Social Interactions as Recursive MDPs. (2021).PDF icon 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).PDF icon CBMM-Memo-128.pdf (2.91 MB)
2019
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).
Barbu, A. et al. ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models. Neural Information Processing Systems (NeurIPS 2019) (2019).PDF icon 9142-objectnet-a-large-scale-bias-controlled-dataset-for-pushing-the-limits-of-object-recognition-models.pdf (16.31 MB)

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