Learning invariant representations and applications to face verification. - Qianli Liao; Joel Z. Leibo; Tomaso A. Poggio (NIPS, 2013)

Can a biologically-plausible hierarchy effectively replace face detection, alignment, and recognition pipelines? - Qianli Liao; Joel Z. Leibo; Youssef Mroueh; Tomaso A. Poggio (CoRR, 2013)

Subtasks of Unconstrained Face Recognition. - Joel Z. Leibo; Qianli Liao; Tomaso A. Poggio (VISAPP (2), 2014)

Unsupervised learning of clutter-resistant visual representations from natural videos. - Qianli Liao; Joel Z. Leibo; Tomaso A. Poggio (CoRR, 2014)

The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex. - Joel Z. Leibo; Qianli Liao; Fabio Anselmi; Tomaso A. Poggio (PLoS Comput. Biol., 2015)

How Important Is Weight Symmetry in Backpropagation? - Qianli Liao; Joel Z. Leibo; Tomaso A. Poggio (AAAI, 2016)

View-tolerant face recognition and Hebbian learning imply mirror-symmetric neural tuning to head orientation. - Joel Z. Leibo; Qianli Liao; Winrich Freiwald; Fabio Anselmi; Tomaso A. Poggio (CoRR, 2016)

Streaming Normalization: Towards Simpler and More Biologically-plausible Normalizations for Online and Recurrent Learning. - Qianli Liao; Kenji Kawaguchi; Tomaso A. Poggio (CoRR, 2016)

Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex. - Qianli Liao; Tomaso A. Poggio (CoRR, 2016)

Learning Real and Boolean Functions: When Is Deep Better Than Shallow. - Hrushikesh N. Mhaskar; Qianli Liao; Tomaso A. Poggio (CoRR, 2016)

When and Why Are Deep Networks Better Than Shallow Ones? - Hrushikesh N. Mhaskar; Qianli Liao; Tomaso A. Poggio (AAAI, 2017)

Compression of Deep Neural Networks for Image Instance Retrieval. - Vijay Chandrasekhar 001; Jie Lin 001; Qianli Liao; Olivier Morère; Antoine Veillard; Ling-Yu Duan; Tomaso A. Poggio (DCC, 2017)

Theory II: Landscape of the Empirical Risk in Deep Learning. - Tomaso A. Poggio; Qianli Liao (CoRR, 2017)

Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review. - Tomaso A. Poggio; Hrushikesh N. Mhaskar; Lorenzo Rosasco; Brando Miranda; Qianli Liao (Int. J. Autom. Comput., 2017)

Theory of Deep Learning III: explaining the non-overfitting puzzle. - Tomaso A. Poggio; Kenji Kawaguchi; Qianli Liao; Brando Miranda; Lorenzo Rosasco; Xavier Boix; Jack Hidary; Hrushikesh N. Mhaskar (CoRR, 2018)

Theory of Deep Learning IIb: Optimization Properties of SGD. - Chiyuan Zhang; Qianli Liao; Alexander Rakhlin; Brando Miranda; Noah Golowich; Tomaso A. Poggio (CoRR, 2018)

Theory IIIb: Generalization in Deep Networks. - Tomaso A. Poggio; Qianli Liao; Brando Miranda; Andrzej Banburski; Xavier Boix; Jack Hidary (CoRR, 2018)

A Surprising Linear Relationship Predicts Test Performance in Deep Networks. - Qianli Liao; Brando Miranda; Andrzej Banburski; Jack Hidary; Tomaso A. Poggio (CoRR, 2018)

Biologically-Plausible Learning Algorithms Can Scale to Large Datasets. - Will Xiao; Honglin Chen; Qianli Liao; Tomaso A. Poggio (ICLR (Poster), 2019)

Theory III: Dynamics and Generalization in Deep Networks. - Andrzej Banburski; Qianli Liao; Brando Miranda; Lorenzo Rosasco; Bob Liang; Jack Hidary; Tomaso A. Poggio (CoRR, 2019)

Theoretical Issues in Deep Networks: Approximation, Optimization and Generalization. - Tomaso A. Poggio; Andrzej Banburski; Qianli Liao (CoRR, 2019)

Hierarchically Local Tasks and Deep Convolutional Networks. - Arturo Deza; Qianli Liao; Andrzej Banburski; Tomaso A. Poggio (CoRR, 2020)

Explicit regularization and implicit bias in deep network classifiers trained with the square loss. - Tomaso A. Poggio; Qianli Liao (CoRR, 2021)

Self-Assembly of a Biologically Plausible Learning Circuit. - Qianli Liao; Liu Ziyin 001; Yulu Gan; Brian Cheung; Mark Harnett; Tomaso A. Poggio (CoRR, 2024)

Understanding Neural Networks from Theoretical and Biological Perspectives - Qianli Liao (2024)