Publication

Found 174 results
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2022
Guo, C. et al. Adversarially trained neural representations may already be as robust as corresponding biological neural representations. arXiv (2022).
Guo, C. et al. Adversarially trained neural representations may already be as robust as corresponding biological neural representations. arXiv (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).
Dapello, J. et al. Aligning Model and Macaque Inferior Temporal Cortex Representations Improves Model-to-Human Behavioral Alignment and Adversarial Robustness. bioRxiv (2022).
Dapello, J. et al. Aligning Model and Macaque Inferior Temporal Cortex Representations Improves Model-to-Human Behavioral Alignment and Adversarial Robustness. bioRxiv (2022).
Dobs, K., Martinez-Trujillo, J., Kell, A. J. E. & Kanwisher, N. Brain-like functional specialization emerges spontaneously in deep neural networks. Science Advances 8, (2022).
Dellaferrera, G. & Kreiman, G. Error-driven Input Modulation: Solving the Credit Assignment Problem without a Backward Pass. Proceedings of the 39th International Conference on Machine Learning, PMLR 162, 4937-4955 (2022).PDF icon dellaferrera22a.pdf (909.91 KB)
Harrington, A. & Deza, A. Finding Biological Plausibility for Adversarially Robust Features via Metameric Tasks. International Conference on Learning Representations (ICLR) (2022). at <https://openreview.net/forum?id=yeP_zx9vqNm>
Sherman, M. A. et al. Genome-wide mapping of somatic mutation rates uncovers drivers of cancerAbstract. Nature Biotechnology 40, 1634 - 1643 (2022).
Berrios, W. & Deza, A. Joint rotational invariance and adversarial training of a dual-stream Transformer yields state of the art Brain-Score for Area V4. BrainScore Workshop at COSYNE (2022). at <https://openreview.net/pdf?id=SOulrWP-Xb5>
Bagus, A. Marliawaty, Marques, T., Sanghavi, S., DiCarlo, J. J. & Schrimpf, M. Primate Inferotemporal Cortex Neurons Generalize Better to Novel Image Distributions Than Analogous Deep Neural Networks Units. NeurIPS (2022). at <https://openreview.net/forum?id=iPF7mhoWkOl>
Quality Early Learning: Nurturing Children's Potential. (The World Bank, 2022). doi:10.1596/978-1-4648-1795-3
Yamada, M., D'Amario, V., Takemoto, K., Boix, X. & Sasaki, T. Transformer Module Networks for Systematic Generalization in Visual Question Answering. (2022).PDF icon CBMM-Memo-121.pdf (1.06 MB)PDF icon 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).
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
Kar, K., Schrimpf, M., Schmidt, K. & DiCarlo, J. J. Chemogenetic suppression of macaque V4 neurons produces retinotopically specific deficits in downstream IT neural activity patterns and core object recognition behavior. Journal of Vision 21, (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>
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>
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).PDF icon 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).PDF icon CBMM Memo 115.pdf (9.56 MB)PDF icon arXiv version (23.05 MB)
Kunhardt, O., Deza, A. & Poggio, T. The Effects of Image Distribution and Task on Adversarial Robustness. (2021).PDF icon CBMM_Memo_116.pdf (5.44 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>

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