Publications

CBMM Memos were established in 2014 as a mechanism for our center to share research results with the wider scientific community. Click here to read more about the memos and to see a full list of the memos.

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Found 13 results

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Filters: Author is Xavier Boix [Clear All Filters]

2016

Luo, Y., Boix, X., Roig, G., Poggio, T. & Zhao, Q. Foveation-based Mechanisms Alleviate Adversarial Examples. (2016).

cbmm_memo_044.pdf (11.48 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).

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>

paper.pdf (963.87 KB)

Poggio, T. et al. Theory of Deep Learning III: explaining the non-overfitting puzzle. (2017).

CBMM-Memo-073.pdf (2.65 MB)

CBMM Memo 073 v2 (revised 1/15/2018) (2.81 MB)

CBMM Memo 073 v3 (revised 1/30/2018) (2.72 MB)

CBMM Memo 073 v4 (revised 12/30/2018) (575.72 KB)

2018

Villalobos, K. M. et al. Can Deep Neural Networks Do Image Segmentation by Understanding Insideness?. (2018).

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>

fake-news-paper-NIPS.pdf (147.36 KB)

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).

CBMM-Memo-093.pdf (2.99 MB)

2020

Madan, S. et al. On the Capability of Neural Networks to Generalize to Unseen Category-Pose Combinations. (2020).

CBMM-Memo-111.pdf (9.76 MB)

Villalobos, K. M. et al. Do Neural Networks for Segmentation Understand Insideness?. (2020).

CBMM-Memo-105.pdf (4.63 MB)

CBMM Memo 105 v2 (July 2, 2020) (3.2 MB)

CBMM Memo 105 v3 (January 25, 2022) (8.33 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>

1912.04783.pdf (6.69 MB)

2022

Sakai, A. et al. Three approaches to facilitate DNN generalization to objects in out-of-distribution orientations and illuminations. (2022).

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).

CBMM-Memo-121.pdf (1.06 MB)

version 2 (3/22/2023) (1.33 MB)