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Qianli Liao
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2020 – today
- 2024
- [b1]Qianli Liao:
Understanding Neural Networks from Theoretical and Biological Perspectives. MIT, USA, 2024 - 2021
- [i19]Tomaso A. Poggio, Qianli Liao:
Explicit regularization and implicit bias in deep network classifiers trained with the square loss. CoRR abs/2101.00072 (2021) - 2020
- [i18]Arturo Deza, Qianli Liao, Andrzej Banburski, Tomaso A. Poggio:
Hierarchically Local Tasks and Deep Convolutional Networks. CoRR abs/2006.13915 (2020)
2010 – 2019
- 2019
- [c6]Will Xiao, Honglin Chen, Qianli Liao, Tomaso A. Poggio:
Biologically-Plausible Learning Algorithms Can Scale to Large Datasets. ICLR (Poster) 2019 - [i17]Andrzej Banburski, Qianli Liao, Brando Miranda, Lorenzo Rosasco, Bob Liang, Jack Hidary, Tomaso A. Poggio:
Theory III: Dynamics and Generalization in Deep Networks. CoRR abs/1903.04991 (2019) - [i16]Tomaso A. Poggio, Andrzej Banburski, Qianli Liao:
Theoretical Issues in Deep Networks: Approximation, Optimization and Generalization. CoRR abs/1908.09375 (2019) - 2018
- [i15]Tomaso A. Poggio, Kenji Kawaguchi, Qianli Liao, Brando Miranda, Lorenzo Rosasco, Xavier Boix, Jack Hidary, Hrushikesh N. Mhaskar:
Theory of Deep Learning III: explaining the non-overfitting puzzle. CoRR abs/1801.00173 (2018) - [i14]Chiyuan Zhang, Qianli Liao, Alexander Rakhlin, Brando Miranda, Noah Golowich, Tomaso A. Poggio:
Theory of Deep Learning IIb: Optimization Properties of SGD. CoRR abs/1801.02254 (2018) - [i13]Tomaso A. Poggio, Qianli Liao, Brando Miranda, Andrzej Banburski, Xavier Boix, Jack Hidary:
Theory IIIb: Generalization in Deep Networks. CoRR abs/1806.11379 (2018) - [i12]Qianli Liao, Brando Miranda, Andrzej Banburski, Jack Hidary, Tomaso A. Poggio:
A Surprising Linear Relationship Predicts Test Performance in Deep Networks. CoRR abs/1807.09659 (2018) - [i11]Will Xiao
, Honglin Chen, Qianli Liao, Tomaso A. Poggio:
Biologically-plausible learning algorithms can scale to large datasets. CoRR abs/1811.03567 (2018) - 2017
- [j2]Tomaso A. Poggio
Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review. Int. J. Autom. Comput. 14(5): 503-519 (2017) - [c5]Hrushikesh N. Mhaskar, Qianli Liao, Tomaso A. Poggio:
When and Why Are Deep Networks Better Than Shallow Ones? AAAI 2017: 2343-2349 - [c4]Vijay Chandrasekhar, Jie Lin, Qianli Liao, Olivier Morère, Antoine Veillard, Ling-Yu Duan, Tomaso A. Poggio:
Compression of Deep Neural Networks for Image Instance Retrieval. DCC 2017: 300-309 - [i10]Vijay Chandrasekhar, Jie Lin, Qianli Liao, Olivier Morère, Antoine Veillard, Ling-Yu Duan, Tomaso A. Poggio:
Compression of Deep Neural Networks for Image Instance Retrieval. CoRR abs/1701.04923 (2017) - [i9]Tomaso A. Poggio, Qianli Liao:
Theory II: Landscape of the Empirical Risk in Deep Learning. CoRR abs/1703.09833 (2017) - 2016
- [c3]Qianli Liao, Joel Z. Leibo, Tomaso A. Poggio:
How Important Is Weight Symmetry in Backpropagation? AAAI 2016: 1837-1844 - [i8]Hrushikesh N. Mhaskar, Qianli Liao, Tomaso A. Poggio:
Learning Real and Boolean Functions: When Is Deep Better Than Shallow. CoRR abs/1603.00988 (2016) - [i7]Qianli Liao, Tomaso A. Poggio:
Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex. CoRR abs/1604.03640 (2016) - [i6]Joel Z. Leibo, Qianli Liao, Winrich Freiwald, Fabio Anselmi, Tomaso A. Poggio:
View-tolerant face recognition and Hebbian learning imply mirror-symmetric neural tuning to head orientation. CoRR abs/1606.01552 (2016) - [i5]Qianli Liao, Kenji Kawaguchi, Tomaso A. Poggio:
Streaming Normalization: Towards Simpler and More Biologically-plausible Normalizations for Online and Recurrent Learning. CoRR abs/1610.06160 (2016) - [i4]Tomaso A. Poggio, Hrushikesh N. Mhaskar, Lorenzo Rosasco, Brando Miranda, Qianli Liao:
Why and When Can Deep - but Not Shallow - Networks Avoid the Curse of Dimensionality: a Review. CoRR abs/1611.00740 (2016) - 2015
- [j1]Joel Z. Leibo, Qianli Liao, Fabio Anselmi
The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex. PLoS Comput. Biol. 11(10) (2015) - [i3]Qianli Liao, Joel Z. Leibo, Tomaso A. Poggio:
How Important is Weight Symmetry in Backpropagation? CoRR abs/1510.05067 (2015) - 2014
- [c2]Joel Z. Leibo, Qianli Liao, Tomaso A. Poggio:
Subtasks of Unconstrained Face Recognition. VISAPP (2) 2014: 113-121 - [i2]Qianli Liao, Joel Z. Leibo, Tomaso A. Poggio:
Unsupervised learning of clutter-resistant visual representations from natural videos. CoRR abs/1409.3879 (2014) - 2013
- [c1]Qianli Liao, Joel Z. Leibo, Tomaso A. Poggio:
Learning invariant representations and applications to face verification. NIPS 2013: 3057-3065 - [i1]Qianli Liao, Joel Z. Leibo, Youssef Mroueh, Tomaso A. Poggio:
Can a biologically-plausible hierarchy effectively replace face detection, alignment, and recognition pipelines? CoRR abs/1311.4082 (2013)
Coauthor Index
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