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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Ren, Z., Wang, C. & Yuille, A. Scene-Domain Active Part Models for Object Representation. IEEE International Conference on Computer Vision (ICCV) 2497 - 2505 (2015). doi:10.1109/ICCV.2015.287

Ren_ICCV15.pdf (3.37 MB)

Wang, Y. - S., Liu, C., Zeng, X. & Yuille, A. Scene Graph Parsing as Dependency Parsing. (2018).

CBMM-Memo-082.pdf (869 KB)

Han, Y., Roig, G., Geiger, G. & Poggio, T. Scale and translation-invariance for novel objects in human vision. Scientific Reports 10, (2020).

s41598-019-57261-6.pdf (1.46 MB)

Montagna, F., Noceti, N., Rosasco, L., Zhang, K. & Locatello, F. Scalable Causal Discovery with Score Matching. NeurIPS 2022 (2022). at <https://openreview.net/forum?id=v56PHv_W2A>

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Tang, H., Buia, C., Madsen, J., Anderson, W. S. & Kreiman, G. A role for recurrent processing in object completion: neurophysiological, psychophysical and computational evidence. (2014).

CBMM-Memo-009.pdf (4.21 MB)

Cheney, N., Schrimpf, M. & Kreiman, G. On the Robustness of Convolutional Neural Networks to Internal Architecture and Weight Perturbations. (2017).

CBMM-Memo-065.pdf (687.76 KB)

Gaziv, G., Lee, M. J. & DiCarlo, J. J. Robustified ANNs Reveal Wormholes Between Human Category Percepts. arXiv (2023). at <https://arxiv.org/abs/2308.06887>

Casper, S., Nadeau, M., Hadfield-Menell, D. & Kreiman, G. Robust Feature-Level Adversaries are Interpretability Tools. NeurIPS (2022). at <https://openreview.net/forum?id=lQ--doSB2o>

8789_robust_feature_level_adversari.pdf (3.79 MB)

Wang, C., Wang, Y., Lin, Z., Yuille, A. & Gao, W. Robust Estimation of 3D Human Poses from a Single Image. (2014).

CBMM-Memo-013.pdf (510.23 KB)

Meyers, E. Review of the CBMM workshop on the Turing++ Question: 'who is there?'. (2016).

Review of the CBMM workshop on the Turing++ Question- 'who is there?' .pdf (555.71 KB)

Nickel, M., Murphy, K., Tresp, V. & Gabrilovich, E. A Review of Relational Machine Learning for Knowledge Graphs: From Multi-Relational Link Prediction to Automated Knowledge Graph Construction. (2015).

CBMM Memo No. 028 (878.56 KB)

Nickel, M., Murphy, K., Tresp, V. & Gabrilovich, E. A Review of Relational Machine Learning for Knowledge Graphs. Proceedings of the IEEE 104, 11 - 33 (2016).

1503.00759v3.pdf (1.53 MB)

Ceola, F., Rosasco, L., Natale, L. & Ceola, F. RESPRECT: Speeding-up Multi-Fingered Grasping With Residual Reinforcement LearningRESPRECT: Speeding-Up Multi-Fingered Grasping With Residual Reinforcement Learning_supp1-3363532.mp4. IEEE Robotics and Automation Letters 9, 3045 - 3052 (2024).

Gerstenberg, T., Halpern, J. Y. & Tenenbaum, J. B. Responsibility judgments in voting scenarios. Annual Meeting of the Cognitive Science Society (CogSci) 788-793 (2015). at <https://mindmodeling.org/cogsci2015/papers/0143/index.html>

Gerstenberg_paper0143.pdf (651.82 KB)

Richardson, H. et al. Response patterns in the developing social brain are organized by social and emotion features and disrupted in children diagnosed with autism spectrum disorder. Cortex 125, 12 - 29 (2020).

Poggio, T. Is Research in Intelligence an Existential Risk?. (2014).

Is Research in Intelligence an Existential Risk.pdf (571.42 KB)

Cohen, M. A. et al. Representational similarity precedes category selectivity in the developing ventral visual pathway. NeuroImage 197, 565 - 574 (2019).

Anselmi, F. & Poggio, T. Representation Learning in Sensory Cortex: a theory. (2014).

CBMM-Memo-026_neuron_ver45.pdf (1.35 MB)

Anselmi, F. & Poggio, T. Representation Learning in Sensory Cortex: a theory. IEEE Access 1 - 1 (2022). doi:10.1109/ACCESS.2022.3208603

Representation_Learning_in_Sensory_Cortex_a_theory.pdf (1.17 MB)

Tacchetti, A., Voinea, S., Evangelopoulos, G. & Poggio, T. Representation Learning from Orbit Sets for One-shot Classification. AAAI Spring Symposium Series, Science of Intelligence (2017). at <https://www.aaai.org/ocs/index.php/SSS/SSS17/paper/view/15357>

Dillon, M. R. & Spelke, E. S. Reorientation ability predicts early spatial symbol reading. 2015 Society for Research in Child Development Biennial Meeting (2015).

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).

1806.01203.pdf (1022.51 KB)

Gershman, S. J. & Daw, N. D. Reinforcement learning and episodic memory in humans and animals: an integrative framework. Annual Review of Psychology 68, (2017).

GershmanDaw17.pdf (422.11 KB)

Nickel, M., Jiang, X. & Tresp, V. Advances in Neural Information Processing Systems 27 1179–1187 (Curran Associates, Inc., 2014). at <http://papers.nips.cc/paper/5448-reducing-the-rank-in-relational-factorization-models-by-including-observable-patterns.pdf>

Liu, C. et al. Recurrent Multimodal Interaction for Referring Image Segmentation. (2018).

CBMM-Memo-079.pdf (10.16 MB)

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