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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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Filters: Author is Qianli Liao [Clear All Filters]

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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., Leibo, J. Z., Mroueh, Y. & Poggio, T. Can a biologically-plausible hierarchy effectively replace face detection, alignment, and recognition pipelines?. (2014).

CBMM-Memo-003.pdf (963.66 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?. (2015).

1510.05067v3.pdf (615.32 KB)

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

CBMM Memo No. 047 (1.29 MB)

Liao, Q., Kawaguchi, K. & Poggio, T. Streaming Normalization: Towards Simpler and More Biologically-plausible Normalizations for Online and Recurrent Learning. (2016).

CBMM-Memo-057.pdf (1.27 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., 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).

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

CBMM-Memo-152.pdf (1.84 MB)