Qianli Liao

According to our database, Qianli Liao authored at least 23 papers between 2013 and 2021.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2021

Explicit regularization and implicit bias in deep network classifiers trained with the square loss.

Tomaso A. Poggio, Qianli Liao

CoRR, 2021

2020

Hierarchically Local Tasks and Deep Convolutional Networks.

Arturo Deza, Qianli Liao, Andrzej Banburski, Tomaso A. Poggio

CoRR, 2020

2019

Theoretical Issues in Deep Networks: Approximation, Optimization and Generalization.

Tomaso A. Poggio, Andrzej Banburski, Qianli Liao

CoRR, 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

Biologically-Plausible Learning Algorithms Can Scale to Large Datasets.

Will Xiao, Honglin Chen, Qianli Liao, Tomaso A. Poggio

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

A Surprising Linear Relationship Predicts Test Performance in Deep Networks.

Qianli Liao, Brando Miranda, Andrzej Banburski, Jack Hidary, 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

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 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

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 II: Landscape of the Empirical Risk in Deep Learning.

Tomaso A. Poggio, Qianli Liao

CoRR, 2017

Compression of Deep Neural Networks for Image Instance Retrieval.

Vijay Chandrasekhar, Jie Lin, Qianli Liao, Olivier Morère, Antoine Veillard, Ling-Yu Duan, Tomaso A. Poggio

Proceedings of the 2017 Data Compression Conference, 2017

When and Why Are Deep Networks Better Than Shallow Ones?

Hrushikesh N. Mhaskar, Qianli Liao, Tomaso A. Poggio

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016

Learning Real and Boolean Functions: When Is Deep Better Than Shallow.

Hrushikesh N. Mhaskar, Qianli Liao, Tomaso A. Poggio

CoRR, 2016

Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex.

Qianli Liao, 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

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

How Important Is Weight Symmetry in Backpropagation?

Qianli Liao, Joel Z. Leibo, Tomaso A. Poggio

Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

2015

The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex.

Joel Z. Leibo, Qianli Liao, Fabio Anselmi, Tomaso A. Poggio

PLoS Comput. Biol., 2015

2014

Unsupervised learning of clutter-resistant visual representations from natural videos.

Qianli Liao, Joel Z. Leibo, Tomaso A. Poggio

CoRR, 2014

Subtasks of Unconstrained Face Recognition.

Joel Z. Leibo, Qianli Liao, Tomaso A. Poggio

Proceedings of the VISAPP 2014, 2014

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

Learning invariant representations and applications to face verification.

Qianli Liao, Joel Z. Leibo, Tomaso A. Poggio

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013