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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2022

Sakai, A. et al. Three approaches to facilitate DNN generalization to objects in out-of-distribution orientations and illuminations. (2022).

CBMM-Memo-119.pdf (31.08 MB)

Tazi, Y., Berger, M. & Freiwald, W. A. Towards an objective characterization of an individual's facial movements using Self-Supervised Person-Specific-Models. arXiv (2022). at <https://arxiv.org/abs/2211.08279>

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

CBMM-Memo-132.pdf (1.15 MB)

Kuo, Y. - L. et al. Trajectory Prediction with Linguistic Representations. 2022 IEEE International Conference on Robotics and Automation (ICRA) (2022). doi:10.1109/ICRA46639.2022.9811928

Yamada, M., D'Amario, V., Takemoto, K., Boix, X. & Sasaki, T. Transformer Module Networks for Systematic Generalization in Visual Question Answering. (2022).

CBMM-Memo-121.pdf (1.06 MB)

version 2 (3/22/2023) (1.33 MB)

Gartstein, M. A. et al. Using machine learning to understand age and gender classification based on infant temperament. PLOS ONE 17, e0266026 (2022).

Gartstein, M. A. et al. Using machine learning to understand age and gender classification based on infant temperament. PLOS ONE 17, e0266026 (2022).

Gartstein, M. A. et al. Using machine learning to understand age and gender classification based on infant temperament. PLOS ONE 17, e0266026 (2022).

Bill, J., Gershman, S. J. & Drugowitsch, J. Visual motion perception as online hierarchical inference. Nature Communications 13, (2022).

Madan, S. et al. When and how convolutional neural networks generalize to out-of-distribution category–viewpoint combinations. Nature Machine Intelligence 4, 146 - 153 (2022).

Madan, S. et al. When and how convolutional neural networks generalize to out-of-distribution category–viewpoint combinations. Nature Machine Intelligence 4, 146 - 153 (2022).

2021

Shu, T. et al. AGENT: A Benchmark for Core Psychological Reasoning. Proceedings of the 38th International Conference on Machine Learning (2021).

Baidya, A., Dapello, J., DiCarlo, J. J. & Marques, T. Combining Different V1 Brain Model Variants to Improve Robustness to Image Corruptions in CNNs. NeurIPS 2021 (2021). at <https://nips.cc/Conferences/2021/ScheduleMultitrack?event=41268>

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., Katz, B. & Barbu, A. Compositional RL Agents That Follow Language Commands in Temporal Logic. Frontiers in Robotics and AI 8, (2021).

frobt-08-689550.pdf (1.57 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)

N. Murty, A. Ratan, Bashivan, P., Abate, A., DiCarlo, J. J. & Kanwisher, N. Computational models of category-selective brain regions enable high-throughput tests of selectivity. Nature Communications 12, (2021).

s41467-021-25409-6.pdf (6.47 MB)

Banburski, A., De La Torre, F., Pant, N., Shastri, I. & Poggio, T. Distribution of Classification Margins: Are All Data Equal?. (2021).

CBMM Memo 115.pdf (9.56 MB)

arXiv version (23.05 MB)

Xu, M. et al. Dynamics and Neural Collapse in Deep Classifiers trained with the Square Loss. (2021).

v1.0 (4.61 MB)

v1.4corrections to generalization section (5.85 MB)

v1.7Small edits (22.65 MB)

Gant, J., Banburski, A., Deza, A. & Poggio, T. Evaluating the Adversarial Robustness of a Foveated Texture Transform Module in a CNN. NeurIPS 2021 (2021). at <https://nips.cc/Conferences/2021/Schedule?showEvent=21868>

Yang, C. et al. Evolutionary and biomedical insights from a marmoset diploid genome assembly. Nature (2021). doi:10.1038/s41586-021-03535-x

Yang, C. et al. Evolutionary and biomedical insights from a marmoset diploid genome assembly. Nature (2021). doi:10.1038/s41586-021-03535-x

Yang, C. et al. Evolutionary and biomedical insights from a marmoset diploid genome assembly. Nature (2021). doi:10.1038/s41586-021-03535-x

Yang, C. et al. Evolutionary and biomedical insights from a marmoset diploid genome assembly. Nature (2021). doi:10.1038/s41586-021-03535-x

McNamee, D., Stachenfeld, K., Botvinick, M. M. & Gershman, S. J. Flexible modulation of sequence generation in the entorhinal-hippocampal system. Nature Neuroscience (2021). doi:10.1038/s41593-021-00831-7

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