Publication

Found 174 results
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2021
Dasgupta, I. & Gershman, S. J. Memory as a Computational Resource. Trends in Cognitive Sciences 25, 240 - 251 (2021).
Marques, T., Schrimpf, M. & DiCarlo, J. J. Multi-scale hierarchical neural network models that bridge from single neurons in the primate primary visual cortex to object recognition behavior. bioRxiv (2021).PDF icon 2021.03.01.433495v2.full_.pdf (3.23 MB)
Liu, Y. et al. Temporally delayed linear modelling (TDLM) measures replay in both animals and humans. eLife 10, (2021).
Du, Y., Smith, K. A., Ullman, T., Tenenbaum, J. B. & Wu, J. Unsupervised Discovery of 3D Physical Objects. International Conference on Learning Representations (2021). at <https://openreview.net/forum?id=lf7st0bJIA5>
Wang, B., Mayo, D., Deza, A., Barbu, A. & Conwell, C. On the use of Cortical Magnification and Saccades as Biological Proxies for Data Augmentation. Shared Visual Representations in Human and Machine Intelligence (SVRHM) Workshop at NeurIPS (2021). at <https://openreview.net/forum?id=Rpazl253IHb>
Li, C. & Deza, A. What Matters In Branch Specialization? Using a Toy Task to Make Predictions. Shared Visual Representations in Human and Machine Intelligence (SVRHM) Workshop at NeurIPS (2021). at <https://openreview.net/forum?id=0kPS1i6wict>
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).
2023
Melloni, L. et al. An adversarial collaboration protocol for testing contrasting predictions of global neuronal workspace and integrated information theory. PLOS ONE 18, e0268577 (2023).PDF icon journal.pone_.0268577.pdf (1.99 MB)

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