Publication

Found 910 results
Author [ Title(Desc)] Type Year
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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>
Kanwisher, N., Khosla, M. & Dobs, K. Using artificial neural networks to ask ‘why’ questions of minds and brains. Trends in Neurosciences 46, 240 - 254 (2023).
Kamps, F. S., Richardson, H., N. Murty, A. Ratan, Kanwisher, N. & Saxe, R. Using child‐friendly movie stimuli to study the development of face, place, and object regions from age 3 to 12 years. Human Brain Mapping (2022). doi:10.1002/hbm.25815
Powell, L. J., Deen, B., Guo, L. & Saxe, R. Using fNIRS to Map Functional Specificity in the Infant Brain: An fROI Approach. (2015).PDF icon SRCD2015_NIRS_poster.pdf (2.14 MB)
Gartstein, M. A. et al. Using machine learning to understand age and gender classification based on infant temperament. PLOS ONE 17, e0266026 (2022).
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>
Ullman, S. Using neuroscience to develop artificial intelligence. Science 363, 692 - 693 (2019).
Dobs, K., Kell, A. J. E., Martinez-Trujillo, J., Cohen, M. & Kanwisher, N. Using task-optimized neural networks to understand why brains have specialized processing for faces . Computational and Systems Neurosciences (2020).
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Bongiorno, C. et al. Vector-based pedestrian navigation in cities. Nature Computational Science 1, 678 - 685 (2021).PDF icon s43588-021-00130-y.pdf (1.96 MB)
Hartshorne, J. K. VerbCorner: Testing theories of argument structure through crowdsourcing. Workshop on Events in Language (2016).PDF icon VerbCorner_EventsInLanguage.pdf (1.14 MB)
Leibo, J. Z., Liao, Q., Anselmi, F., Freiwald, W. A. & Poggio, T. View-Tolerant Face Recognition and Hebbian Learning Imply Mirror-Symmetric Neural Tuning to Head Orientation. Current Biology 27, 1-6 (2017).
Leibo, J. Z., Liao, Q., Freiwald, W. A., Anselmi, F. & Poggio, T. View-tolerant face recognition and Hebbian learning imply mirror-symmetric neural tuning to head orientation. (2016).PDF icon faceMirrorSymmetry_memo_ver01.pdf (3.93 MB)
Ma, K. - T., Sim, T. & Kankanhalli, M. VIP: A unifying framework for eye-gaze research. (2013). at <http://mmas.comp.nus.edu.sg/VIP.html>
Phillips-Jones, T., Coronel, S. Otero, Sani, I. & Freiwald, W. A. A Virtual Reality Experimental Approach for Studying How the Brain Implements Attentive Behaviors. Tri-Institute 2019 Gateways to the Laboratory Summer Program (2019).
de la Rosa, S. et al. Visual categorization of social interactions. Visual Cognition 22, (2015).
Mormann, F. et al. Single neuron studies of the human brain. Probing cognition (2014).
Rosenfeld, A. & Ullman, S. Visual Concept Recognition and Localization via Iterative Introspection. . Asian Conference on Computer Vision (2016).PDF icon Focusing on parts of interest  (910.14 KB)
Han, C., Mao, J., Gan, C., Tenenbaum, J. B. & Wu, J. Visual Concept-Metaconcept Learning. Neural Information Processing Systems (NeurIPS 2019) (2019).PDF icon 8745-visual-concept-metaconcept-learning.pdf (1.92 MB)
Wang, J. et al. Visual concepts and compositional voting. (2018).PDF icon CBMM-Memo-087.pdf (3.37 MB)
Wang, J. et al. Visual Concepts and Compositional Voting. Annals of Mathematical Sciences and Applications (AMSA) 3, 151–188 (2018).
Poggio, T. & Anselmi, F. Visual Cortex and Deep Networks: Learning Invariant Representations. 136 (The MIT Press, 2016). at <https://mitpress.mit.edu/books/visual-cortex-and-deep-networks>
Zarco, W. & Freiwald, W. A. Visual Features for Invariant Coding by Face Selective Neurons . 2019 Conference on Cognitive Computational Neuroscience (CCN) (2019).
Izard, V., Pica, P. & Spelke, E. S. Visual foundations of Euclidean geometry. Cognitive Psychology 136, 101494 (2022).
Bill, J., Gershman, S. J. & Drugowitsch, J. Visual motion perception as online hierarchical inference. Nature Communications 13, (2022).
Gupta, S. Kant, Zhang, M., WU, C. H. I. A. - C. H. I. E. N., Wolfe, J. & Kreiman, G. Visual Search Asymmetry: Deep Nets and Humans Share Similar Inherent Biases. NeurIPS 2021 (2021). at <https://nips.cc/Conferences/2021/Schedule?showEvent=28848>PDF icon gk8091.pdf (2.47 MB)

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