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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CBMM Memos

Ben-Yosef, G., Assif, L. & Ullman, S. Full interpretation of minimal images. (2017).

CBMM Memo 061 v.1 (4.64 MB)

CBMM Memo 061 v.2 (5.41 MB)

Poggio, T. From Marr’s Vision to the Problem of Human Intelligence. (2021).

CBMM-Memo-118.pdf (362.19 KB)

Poggio, T. From Associative Memories to Powerful Machines. (2021).

v1.0 (1.01 MB)

v1.3Section added August 6 on self attention (3.9 MB)

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

cbmm_memo_044.pdf (11.48 MB)

Ziyin, L., Chuang, I., Galanti, T. & Poggio, T. Formation of Representations in Neural Networks. (2024).

CBMM-Memo-150.pdf (4.03 MB)

Subirana, B., Bagiati, A. & Sarma, S. On the Forgetting of College Academics: at "Ebbinghaus Speed"?. (2017).

CBMM Memo 068-On Forgetting - June 18th 2017 v2.pdf (713.7 KB)

Rangamani, A., Rosasco, L. & Poggio, T. For interpolating kernel machines, the minimum norm ERM solution is the most stable. (2020).

CBMM_Memo_108.pdf (1015.14 KB)

Better bound (without inequalities!) (1.03 MB)

Gan, Y. & Poggio, T. For HyperBFs AGOP is a greedy approximation to gradient descent. (2024).

CBMM-Memo-148.pdf (1.06 MB)

Rangamani, A., Lindegaard, M., Galanti, T. & Poggio, T. Feature learning in deep classifiers through Intermediate Neural Collapse. (2023).

Feature_Learning_memo.pdf (2.16 MB)

Isik, L., Tacchetti, A. & Poggio, T. Fast, invariant representation for human action in the visual system. (2016). at <http://arxiv.org/abs/1601.01358>

CBMM Memo 042 (3.03 MB)

Shalev-Shwartz, S. & Shashua, A. An Exit Strategy from the Covid-19 Lockdown based on Risk-sensitive Resource Allocation. (2020).

CBMM-Memo-106.pdf (431.13 KB)

Kuo, Y. - L., Katz, B. & Barbu, A. Encoding formulas as deep networks: Reinforcement learning for zero-shot execution of LTL formulas. (2020).

CBMM-Memo-125.pdf (2.12 MB)

Poggio, T. A. & Xu, M. On efficiently computable functions, deep networks and sparse compositionality. (2025).

Deep_sparse_networks_approximate_efficiently_computable_functions.pdf (223.15 KB)

Kunhardt, O., Deza, A. & Poggio, T. The Effects of Image Distribution and Task on Adversarial Robustness. (2021).

CBMM_Memo_116.pdf (5.44 MB)

Xu, M. et al. Dynamics and Neural Collapse in Deep Classifiers trained with the Square Loss. (2021).

v1.0 (4.61 MB)

v1.4corrections to generalization section (5.85 MB)

v1.7Small edits (22.65 MB)

Banburski, A. et al. Dreaming with ARC. Learning Meets Combinatorial Algorithms workshop at NeurIPS 2020 (2020).

CBMM Memo 113.pdf (1019.64 KB)

Poggio, T., Kur, G. & Banburski, A. Double descent in the condition number. (2019).

Fixing typos, clarifying error in y, best approach is crossvalidation (837.18 KB)

Incorporated footnote in text plus other edits (854.05 KB)

Deleted previous discussion on kernel regression and deep nets: it will appear, extended, in a separate paper (795.28 KB)

correcting a bad typo (261.24 KB)

Deleted plot of condition number of kernel matrix: we cannot get a double descent curve (769.32 KB)

Berzak, Y., Barbu, A., Harari, D., Katz, B. & Ullman, S. Do You See What I Mean? Visual Resolution of Linguistic Ambiguities. (2016).

memo-51.pdf (2.74 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)

Volokitin, A., Roig, G. & Poggio, T. Do Deep Neural Networks Suffer from Crowding?. (2017).

CBMM-Memo-069.pdf (6.47 MB)

Banburski, A., De La Torre, F., Pant, N., Shastri, I. & Poggio, T. Distribution of Classification Margins: Are All Data Equal?. (2021).

CBMM Memo 115.pdf (9.56 MB)

arXiv version (23.05 MB)

Tacchetti, A., Voinea, S. & Evangelopoulos, G. Discriminate-and-Rectify Encoders: Learning from Image Transformation Sets. (2017).

CBMM-Memo-062.pdf (9.37 MB)

Wang, J. et al. Detecting Semantic Parts on Partially Occluded Objects. (2017).

CBMM-Memo-078.pdf (1.74 MB)

Chen, X. et al. Detect What You Can: Detecting and Representing Objects using Holistic Models and Body Parts. (2014).

CBMM-Memo-015.pdf (974.07 KB)

Zhang, Z., Xie, C., Wang, J., Xie, L. & Yuille, A. DeepVoting: A Robust and Explainable Deep Network for Semantic Part Detection under Partial Occlusion. (2018).

CBMM-Memo-083.pdf (2.32 MB)

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