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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Leibo, J. Z., Liao, Q., Anselmi, F. & Poggio, T. The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex. (2015).

modularity_dataset_ver1.tar.gz (36.14 MB)

Leibo, J. Z., Liao, Q., Anselmi, F. & Poggio, T. The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex. (2014). doi:10.1101/004473

CBMM Memo 004_new.pdf (2.25 MB)

Leibo, J. Z., Liao, Q. & Poggio, T. Subtasks of Unconstrained Face Recognition. (2014).

Leibo_Liao_Poggio_subtasks_VISAPP_2014.pdf (268.69 KB)

Leibo, J. Z., Liao, Q., Freiwald, W. A., Anselmi, F. & Poggio, T. View-tolerant face recognition and Hebbian learning imply mirror-symmetric neural tuning to head orientation. (2016).

faceMirrorSymmetry_memo_ver01.pdf (3.93 MB)

Leonard, J. A., Garcia, A. & Schulz, L. How Adults’ Actions, Outcomes, and Testimony Affect Preschoolers’ Persistence. Child Development (2019). doi:10.1111/cdev.13305

Leonard, J. A., Lee, Y. & Schulz, L. Infants make more attempts to achieve a goal when they see adults persist. Science 357, 1290 - 1294 (2017).

Letizia, M. et al. Learning new physics efficiently with nonparametric methodsAbstract. The European Physical Journal C 82, (2022).

Levine, S., Kleiman-Weiner, M., Schulz, L., Tenenbaum, J. B. & Cushman, F. A. The logic of universalization guides moral judgment. Proceedings of the National Academy of Sciences (PNAS) 202014505 (2020). doi:10.1073/pnas.2014505117

Lewis, O. Structured learning and inference with neural networks and generative models. (2018).

Lewis, O. & Hermann, K. Data for free: Fewer-shot algorithm learning with parametricity data augmentation. ICLR 2019 (2019).

Lewis, O. & Poggio, T. From Neuron to Cognition via Computational Neuroscience (The MIT Press, 2016). at <https://mitpress.mit.edu/neuron-cognition>

Li, Y., Koch, C., Rehg, J. M. & Yuille, A. The Secrets of Salient Object Segmentation. (2014).

CBMM-Memo-014.pdf (1.59 MB)

Li, Y. et al. An approximate representation of objects underlies physical reasoning. psyArXiv (2022). at <https://psyarxiv.com/vebu5/>

Li, C. & Deza, A. What Matters In Branch Specialization? Using a Toy Task to Make Predictions. Shared Visual Representations in Human and Machine Intelligence (SVRHM) Workshop at NeurIPS (2021). at <https://openreview.net/forum?id=0kPS1i6wict>

Liang, T., Poggio, T., Rakhlin, A. & Stokes, J. Fisher-Rao Metric, Geometry, and Complexity of Neural Networks. arXiv.org (2017). at <https://arxiv.org/abs/1711.01530>

1711.01530.pdf (966.99 KB)

Liao, Q. et al. Self-Assembly of a Biologically Plausible Learning Circuit. (2024).

CBMM-Memo-152.pdf (1.84 MB)

Liao, Q., Leibo, J. Z. & Poggio, T. How Important Is Weight Symmetry in Backpropagation?. Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16) (2016). at <https://cbmm.mit.edu/sites/default/files/publications/liao-leibo-poggio.pdf>

Liao, Q. & Poggio, T. Object-Oriented Deep Learning. (2017).

CBMM-Memo-070.pdf (963.54 KB)

Liao, Q., Leibo, J. Z. & Poggio, T. Learning invariant representations and applications to face verification. NIPS 2013 (Advances in Neural Information Processing Systems 26, 2014). at <http://nips.cc/Conferences/2013/Program/event.php?ID=4074>

Liao_Leibo_Poggio_NIPS_2013.pdf (687.06 KB)

Liao, Q. & Poggio, T. Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex. (2016).

CBMM Memo No. 047 (1.29 MB)

Liao, Q., Leibo, J. Z. & Poggio, T. How Important is Weight Symmetry in Backpropagation?. (2015).

1510.05067v3.pdf (615.32 KB)

Liao, Q., Leibo, J. Z. & Poggio, T. Unsupervised learning of clutter-resistant visual representations from natural videos. (2014).

1409.3879v2.pdf (3.64 MB)

Liao, Q., Leibo, J. Z. & Poggio, T. How Important Is Weight Symmetry in Backpropagation?. Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16) (Association for the Advancement of Artificial Intelligence, 2016).

liao-leibo-poggio.pdf (191.91 KB)

Liao, Q., Miranda, B., Hidary, J. & Poggio, T. Classical generalization bounds are surprisingly tight for Deep Networks. (2018).

CBMM-Memo-091.pdf (1.43 MB)

CBMM-Memo-091-v2.pdf (1.88 MB)

Liao, Q., Banburski, A. & Poggio, T. Theories of Deep Learning: Approximation, Optimization and Generalization . TECHCON 2019 (2019).

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