Publication

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Filters: Author is Yena Han  [Clear All Filters]
2022
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)
2020
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)
2019
Zhang, J., Han, Y., Poggio, T. & Roig, G. Eccentricity Dependent Neural Network with Recurrent Attention for Scale, Translation and Clutter Invariance . Vision Science Society (2019).
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
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).
2018
Arend, L. et al. Single units in a deep neural network functionally correspond with neurons in the brain: preliminary results. (2018).PDF icon CBMM-Memo-093.pdf (2.99 MB)
2017
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. Is the Human Visual System Invariant to Translation and Scale?. AAAI Spring Symposium Series, Science of Intelligence (2017).