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Found 906 results
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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>
Harari, D., Gao, T., Kanwisher, N., Tenenbaum, J. B. & Ullman, S. Measuring and modeling the perception of natural and unconstrained gaze in humans and machines. (2016).PDF icon CBMM-Memo-059.pdf (1.71 MB)
Harari, D., Tenenbaum, J. B. & Ullman, S. Discovery and usage of joint attention in images. arXiv.org (2018). at <https://arxiv.org/abs/1804.04604>PDF icon 1804.04604v1.pdf (488.85 KB)
Han, Y., Roig, G., Geiger, G. & Poggio, T. Is the Human Visual System Invariant to Translation and Scale?. AAAI Spring Symposium Series, Science of Intelligence (2017).
Han, Y., Poggio, T. & Cheung, B. System Identification of Neural Systems: If We Got It Right, Would We Know?. Proceedings of the 40th International Conference on Machine Learning, PMLR 202, 12430-12444 (2023).PDF icon han23d.pdf (797.48 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)
Han, Y., Roig, G., Geiger, G. & Poggio, T. On the Human Visual System Invariance to Translation and Scale. Vision Sciences Society (2017).
Han, Y., Roig, G., Geiger, G. & Poggio, T. Properties of invariant object recognition in human one-shot learning suggests a hierarchical architecture different from deep convolutional neural networks. Vision Science Society (2019).
Han, Y., Roig, G., Geiger, G. & Poggio, T. Scale and translation-invariance for novel objects in human vision. Scientific Reports 10, (2020).PDF icon s41598-019-57261-6.pdf (1.46 MB)
Han, Y., Poggio, T. & Cheung, B. System identification of neural systems: If we got it right, would we know?. (2022).PDF icon CBMM-Memo-136.pdf (1.75 MB)
Han, Y., Roig, G., Geiger, G. & Poggio, T. Properties of invariant object recognition in human oneshot learning suggests a hierarchical architecture different from deep convolutional neural networks . Vision Science Society (2019). doi:10.1167/19.10.28d
Hamrick, J. B. et al. Relational inductive bias for physical construction in humans and machines. In Proceedings of the Annual Meeting of the Cognitive Science Society (CogSci 2018) (2018).PDF icon 1806.01203.pdf (1022.51 KB)
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Gupte, A., Banburski, A. & Poggio, T. PCA as a defense against some adversaries. (2022).PDF icon CBMM-Memo-135.pdf (2.58 MB)
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)
Guo, C. et al. Adversarially trained neural representations may already be as robust as corresponding biological neural representations. arXiv (2022).
Grossman, N. et al. Noninvasive Deep Brain Stimulation via Temporally Interfering Electric Fields. Cell 169, 1029 - 1041.e16 (2017).
Goodman, N. D., Tenenbaum, J. B. & Gerstenberg, T. Concepts in a Probabilistic Language of Thought. (2014).PDF icon CBMM-Memo-010.pdf (902.53 KB)
Gómez-Laberge, C., Smolyanskaya, A., Nassi, J. J., Kreiman, G. & Born, R. T. Bottom-up and Top-down Input Augment the Variability of Cortical Neurons. Neuron 91(3), 540-547 (2016).
Golowich, N., Rakhlin, A. & Shamir, O. Size-Independent Sample Complexity of Neural Networks. (2017).PDF icon 1712.06541.pdf (278.77 KB)
Gjata, N. N., Ullman, T. D., Spelke, E. S. & Liu, S. What Could Go Wrong: Adults and Children Calibrate Predictions and Explanations of Others' Actions Based on Relative Reward and Danger. Cognitive Science 46, (2022).
Mao, J. et al. Temporal and Object Quantification Networks. Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (Zhou, Z. - H.) (2021). doi:10.24963/ijcai.2021/386PDF icon 0386.pdf (472.5 KB)
Houlihan, S. Dae, Tenenbaum, J. B. & Saxe, R. The Neural Basis of Mentalizing: Linking Models of Theory of Mind and Measures of Human Brain Activity. 209 - 235 (Springer International Publishing, 2021). doi:10.1007/978-3-030-51890-510.1007/978-3-030-51890-5_11
Gerstenberg, T. & Tenenbaum, J. B. Understanding "almost": Empirical and computational studies of near misses. 38th Annual Meeting of the Cognitive Science Society (2016).PDF icon Understanding almost (Gerstenberg, Tenenbaum, 2016).pdf (4.08 MB)
Gerstenberg, T., Goodman, N. D., Lagnado, D. A. & Tenenbaum, J. B. How, whether, why: Causal judgments as counterfactual contrasts. Annual Meeting of the Cognitive Science Society (CogSci) 782-787 (2015). at <https://mindmodeling.org/cogsci2015/papers/0142/index.html>PDF icon GerstenbergEtAl2015-Cogsci.pdf (2.16 MB)
Gerstenberg, T., Zhou, L., Smith, K. A. & Tenenbaum, J. B. Faulty Towers: A counterfactual simulation model of physical support. Proceedings of the 39th Annual Conference of the Cognitive Science Society (2017).PDF icon Faulty Towers A counterfactual simulation model of physical support, Gerstenberg et al., 2017.pdf (8.75 MB)

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