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Qianli Liao

Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review
2017 · Tomaso Poggio, Hrushikesh Mhaskar, et al. · Machine Intelligence Research · 611
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Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex
2016 · Liao, Qianli, Poggio, Tomaso · arXiv (Cornell University) · 253
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Theoretical issues in deep networks
2020 · Tomaso Poggio, Andrzej Banburski, et al. · Proceedings of the National Academy of Sciences · 195
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When and Why Are Deep Networks Better Than Shallow Ones?
2017 · Hrushikesh Mhaskar, Qianli Liao, et al. · Proceedings of the AAAI Conference on Artificial Intelligence · 190
How Important Is Weight Symmetry in Backpropagation?
2016 · Qianli Liao, Joel Leibo, et al. · Proceedings of the AAAI Conference on Artificial Intelligence · 151
Learning Functions: When Is Deep Better Than Shallow
2016 · Mhaskar, Hrushikesh, Liao, Qianli, et al. · arXiv (Cornell University) · 149
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Theory of Deep Learning III: explaining the non-overfitting puzzle
2017 · Poggio, Tomaso, Kawaguchi, Kenji, et al. · arXiv (Cornell University) · 112
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Biologically-plausible learning algorithms can scale to large datasets
2018 · Xiao, Will, Chen, Honglin, et al. · arXiv (Cornell University) · 58
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Theory II: Landscape of the Empirical Risk in Deep Learning
2017 · Liao, Qianli, Poggio, Tomaso · arXiv (Cornell University) · 56
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Theory of Deep Learning IIb: Optimization Properties of SGD
2018 · Zhang, Chiyuan, Liao, Qianli, et al. · arXiv (Cornell University) · 52
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View-Tolerant Face Recognition and Hebbian Learning Imply Mirror-Symmetric Neural Tuning to Head Orientation
2016 · Joel Z. Leibo, Qianli Liao, et al. · Current Biology · 48
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Learning Real and Boolean Functions: When Is Deep Better Than Shallow
2016 · H. N. Mhaskar, Qianli Liao, et al. · DSpace@MIT (Massachusetts Institute of Technology) · 38
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Explicit regularization and implicit bias in deep network classifiers trained with the square loss
2020 · Poggio, Tomaso, Liao, Qianli · arXiv (Cornell University) · 37
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Complexity control by gradient descent in deep networks
2020 · Tomaso Poggio, Qianli Liao, et al. · Nature Communications · 36
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Learning invariant representations and applications to face verification
2013 · Qianli Liao, Joel Z. Leibo, et al. · DSpace@MIT (Massachusetts Institute of Technology) · 34
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Streaming Normalization: Towards Simpler and More Biologically-plausible Normalizations for Online and Recurrent Learning
2016 · Liao, Qianli, Kawaguchi, Kenji, et al. · arXiv (Cornell University) · 32
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The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex
2015 · Joel Z. Leibo, Qianli Liao, et al. · PLoS Computational Biology · 30
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Theory I: Deep networks and the curse of dimensionality
2018 · T. Poggio, Q. Liao · Bulletin of the Polish Academy of Sciences Technical Sciences · 24
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A Surprising Linear Relationship Predicts Test Performance in Deep Networks
2018 · Liao, Qianli, Miranda, Brando, et al. · arXiv (Cornell University) · 24
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Theory IIIb: Generalization in Deep Networks
2018 · Poggio, Tomaso, Liao, Qianli, et al. · arXiv (Cornell University) · 23
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Compression of Deep Neural Networks for Image Instance Retrieval
2017 · Vijay Chandrasekhar, Jie Lin, et al. · DCC · 21
Subtasks of Unconstrained Face Recognition
2014 · Joel Z. Leibo, Qianli Liao, et al. · 20
Can a biologically-plausible hierarchy effectively replace face detection, alignment, and recognition pipelines?
2013 · Liao, Qianli, Leibo, Joel Z, et al. · arXiv (Cornell University) · 17
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Hierarchical Reasoning Models: Perspectives and Misconceptions
2025 · Ge, Renee, Liao, Qianli, et al. · arXiv (Cornell University) · 13
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Dynamics in Deep Classifiers Trained with the Square Loss: Normalization, Low Rank, Neural Collapse, and Generalization Bounds
2023 · Mengjia Xu, Akshay Rangamani, et al. · Research · 11
Works count: 68
Citation count: 2,319
H-index: 21
I10-index: 25
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