Qianli Liao

Current Advisees

Christian Henn - UROP
Taras Zhylenko - UROP

Past Advisees

Nadya L Balabanska - EIT
Soorajnath Boominathan - EIT
Akhilan Boopathy - EIT
Alex Chen - UROP
Tom Dudzik - UROP
Rene A. Garcia - UROP
Xiaolu Guo - EIT
Parker Hao - EIT
Gabe Hege - Graduate Student
Erwin Hilton - Graduate Student
Zhuoqiao Hong - Graduate Student
Kamoya K. Ikhofua - UROP
Andrea Jessica D. Jaba - UROP
Kriti Jain - EIT
Ivan Jutamulia - Undergraduate Research Assistant
Sule Kahraman - UROP
Bishesh Khadka - UROP
Jessica Liu - EIT
Sofia Luo - UROP
Jordyn L. Mann - EIT
Devin C. Morgan - UROP
Inioluwa A. Oguntola - UROP
Michael Opara - EIT
DaMarcus Patterson - UROP
Vivian Qian - UROP
Alizee Schoen - UROP
Yasmin Siahpoosh - UROP
Prachi Sinha - EIT
David Walter - Graduate Student
Daniel Wrafter - EIT
Alex Yang - UROP
Mingshi Yang - UROP
Kevin Yue - UROP
Queenie Zhang - UROP
Tong Zhao - UROP

Projects

Biologically Plausible Implementations of i-theory for feedforward face recognition and object recognition in the ventral stream

CBMM Publications

Q. Liao, Ziyin, L., Gan, Y., Cheung, B., Harnett, M., and Poggio, T., “Self-Assembly of a Biologically Plausible Learning Circuit”. 2024.
T. Poggio, Liao, Q., and Banburski, A., “Complexity Control by Gradient Descent in Deep Networks”, Nature Communications, vol. 11, 2020.
A. Deza, Liao, Q., Banburski, A., and Poggio, T., “Hierarchically Local Tasks and Deep Convolutional Networks”. 2020.
T. Poggio, Liao, Q., and Xu, M., “Implicit dynamic regularization in deep networks”. 2020.
T. Poggio and Liao, Q., “Explicit regularization and implicit bias in deep network classifiers trained with the square loss”, arXiv, 2020.
T. Poggio, Banburski, A., and Liao, Q., “Theoretical issues in deep networks”, Proceedings of the National Academy of Sciences, p. 201907369, 2020.
W. Xiao, Chen, H., Liao, Q., and Poggio, T., “Biologically-plausible learning algorithms can scale to large datasets.”, in International Conference on Learning Representations, (ICLR 2019), 2019.
A. Banburski, Liao, Q., Miranda, B., Rosasco, L., Hidary, J., and Poggio, T., “Dynamics & Generalization in Deep Networks -Minimizing the Norm”, in NAS Sackler Colloquium on Science of Deep Learning, Washington D.C., 2019.
A. Banburski, Liao, Q., Miranda, B., Rosasco, L., Hidary, J., and Poggio, T., “Weight and Batch Normalization implement Classical Generalization Bounds ”, in ICML, Long Beach/California, 2019.
T. Poggio, Banburski, A., and Liao, Q., “Theoretical Issues in Deep Networks”. 2019.
Q. Liao, Banburski, A., and Poggio, T., “Theories of Deep Learning: Approximation, Optimization and Generalization ”, TECHCON 2019. 2019.
T. Poggio and Liao, Q., “Theory II: Deep learning and optimization”, Bulletin of the Polish Academy of Sciences: Technical Sciences, vol. 66, no. 6, 2018.
T. Poggio and Liao, Q., “Theory I: Deep networks and the curse of dimensionality”, Bulletin of the Polish Academy of Sciences: Technical Sciences, vol. 66, no. 6, 2018.
A. Banburski, Liao, Q., Miranda, B., Poggio, T., Rosasco, L., Hidary, J., and De La Torre, F., “Theory III: Dynamics and Generalization in Deep Networks”. 2018.
Q. Liao, Miranda, B., Hidary, J., and Poggio, T., “Classical generalization bounds are surprisingly tight for Deep Networks”. 2018.
W. Xiao, Chen, H., Liao, Q., and Poggio, T., “Biologically-plausible learning algorithms can scale to large datasets”. 2018.
H. Mhaskar, Liao, Q., and Poggio, T., “When and Why Are Deep Networks Better Than Shallow Ones?”, AAAI-17: Thirty-First AAAI Conference on Artificial Intelligence. 2017.
J. Z. Leibo, Liao, Q., Anselmi, F., Freiwald, W. A., and Poggio, T., “View-Tolerant Face Recognition and Hebbian Learning Imply Mirror-Symmetric Neural Tuning to Head Orientation”, Current Biology, vol. 27, pp. 1-6, 2017.
V. Chandrasekhar, Lin, J., Liao, Q., Morère, O., Veillard, A., Duan, L., and Poggio, T., “Compression of Deep Neural Networks for Image Instance Retrieval”, 2017.
T. Poggio, Mhaskar, H., Rosasco, L., Miranda, B., and Liao, Q., “Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review”, International Journal of Automation and Computing, pp. 1-17, 2017.
T. Poggio and Liao, Q., “Theory II: Landscape of the Empirical Risk in Deep Learning”. 2017.
C. Zhang, Liao, Q., Rakhlin, A., Sridharan, K., Miranda, B., Golowich, N., and Poggio, T., “Musings on Deep Learning: Properties of SGD”. 2017.
Q. Liao and Poggio, T., “Object-Oriented Deep Learning”. 2017.
C. Zhang, Liao, Q., Rakhlin, A., Miranda, B., Golowich, N., and Poggio, T., “Theory of Deep Learning IIb: Optimization Properties of SGD”. 2017.
T. Poggio, Kawaguchi, K., Liao, Q., Miranda, B., Rosasco, L., Boix, X., Hidary, J., and Mhaskar, H., “Theory of Deep Learning III: explaining the non-overfitting puzzle”. 2017.
H. Mhaskar, Liao, Q., and Poggio, T., “Learning Functions: When Is Deep Better Than Shallow”. 2016.
Q. Liao, Leibo, J. Z., and Poggio, T., “How Important Is Weight Symmetry in Backpropagation?”, in Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), Phoenix, AZ., 2016.
Q. Liao and Poggio, T., “Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex”. 2016.
J. Z. Leibo, Liao, Q., Freiwald, W. A., Anselmi, F., and Poggio, T., “View-tolerant face recognition and Hebbian learning imply mirror-symmetric neural tuning to head orientation”. 2016.
Q. Liao, Kawaguchi, K., and Poggio, T., “Streaming Normalization: Towards Simpler and More Biologically-plausible Normalizations for Online and Recurrent Learning”. 2016.
T. Poggio, Mhaskar, H., Rosasco, L., Miranda, B., and Liao, Q., “Theory I: Why and When Can Deep Networks Avoid the Curse of Dimensionality?”. 2016.
Q. Liao, Leibo, J. Z., and Poggio, T., “How Important Is Weight Symmetry in Backpropagation?”, in Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), Phoenix, AZ., 2016.
J. Z. Leibo, Liao, Q., Anselmi, F., and Poggio, T., “The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex”. 2015.
J. Z. Leibo, Liao, Q., Anselmi, F., and Poggio, T., “The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex”, PLOS Computational Biology, vol. 11, no. 10, p. e1004390, 2015.
Q. Liao, Leibo, J. Z., and Poggio, T., “How Important is Weight Symmetry in Backpropagation?”. 2015.
J. Z. Leibo, Liao, Q., and Poggio, T., “Subtasks of Unconstrained Face Recognition”. 9th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. (VISAPP)., Lisbon, Portugal, 2014.
J. Z. Leibo, Liao, Q., and Poggio, T., “Subtasks of unconstrained face recognition”. 2014.
Q. Liao, Leibo, J. Z., and Poggio, T., “Learning invariant representations and applications to face verification”, in NIPS 2013, Lake Tahoe, Nevada, 2014.
Q. Liao, Leibo, J. Z., Mroueh, Y., and Poggio, T., “Can a biologically-plausible hierarchy effectively replace face detection, alignment, and recognition pipelines?”. 2014.
J. Z. Leibo, Liao, Q., Anselmi, F., and Poggio, T., “The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex”. 2014.
Q. Liao, Leibo, J. Z., and Poggio, T., “Unsupervised learning of clutter-resistant visual representations from natural videos.”. 2014.