Publication

Found 13 results
Author Title Type [ Year(Asc)]
Filters: First Letter Of Last Name is B and Author is Xavier Boix  [Clear All Filters]
2022
Sakai, A. et al. Three approaches to facilitate DNN generalization to objects in out-of-distribution orientations and illuminations. (2022).PDF icon CBMM-Memo-119.pdf (31.08 MB)
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)
2021
Casper, S. et al. Frivolous Units: Wider Networks Are Not Really That Wide. AAAI 2021 (2021). at <https://dblp.org/rec/conf/aaai/CasperBDGSVK21.html>PDF icon 1912.04783.pdf (6.69 MB)
2020
Madan, S. et al. On the Capability of Neural Networks to Generalize to Unseen Category-Pose Combinations. (2020).PDF icon CBMM-Memo-111.pdf (9.76 MB)
Villalobos, K. M. et al. Do Neural Networks for Segmentation Understand Insideness?. (2020).PDF icon CBMM-Memo-105.pdf (4.63 MB)PDF icon CBMM Memo 105 v2 (July 2, 2020) (3.2 MB)PDF icon CBMM Memo 105 v3 (January 25, 2022) (8.33 MB)
2018
Villalobos, K. M. et al. Can Deep Neural Networks Do Image Segmentation by Understanding Insideness?. (2018).PDF icon CBMM-Memo-095.pdf (1.96 MB)
O'Brien, N., Latessa, S., Evangelopoulos, G. & Boix, X. The Language of Fake News: Opening the Black-Box of Deep Learning Based Detectors. workshop on "AI for Social Good", NIPS 2018 (2018). at <http://hdl.handle.net/1721.1/120056>PDF icon fake-news-paper-NIPS.pdf (147.36 KB)PDF icon fake-news-paper-NIPS_2018_v2.pdf (147.36 KB)
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
A del Molino, G., Boix, X., Lim, J. & Tan, A. Active Video Summarization: Customized Summaries via On-line Interaction. AAAI Conference on Artificial Intelligence (2017).PDF icon 21-Garcia-del-Molino-14856.pdf (413.77 KB)
Roig, G., Chen, F., Boix, X. & Poggio, T. Eccentricity Dependent Deep Neural Networks for Modeling Human Vision. Vision Sciences Society (2017).
Chen, F., Roig, G., Isik, L., Boix, X. & Poggio, T. Eccentricity Dependent Deep Neural Networks: Modeling Invariance in Human Vision. AAAI Spring Symposium Series, Science of Intelligence (2017). at <https://www.aaai.org/ocs/index.php/SSS/SSS17/paper/view/15360>PDF icon paper.pdf (963.87 KB)
Poggio, T. et al. Theory of Deep Learning III: explaining the non-overfitting puzzle. (2017).PDF icon CBMM-Memo-073.pdf (2.65 MB)PDF icon CBMM Memo 073 v2 (revised 1/15/2018) (2.81 MB)PDF icon CBMM Memo 073 v3 (revised 1/30/2018) (2.72 MB)PDF icon CBMM Memo 073 v4 (revised 12/30/2018) (575.72 KB)
2016
Luo, Y., Boix, X., Roig, G., Poggio, T. & Zhao, Q. Foveation-based Mechanisms Alleviate Adversarial Examples. (2016).PDF icon cbmm_memo_044.pdf (11.48 MB)