Qianli Liao
- Artificial Intelligence top 5%
- Neural Networks and Applications 9
- Stochastic Gradient Optimization Techniques 7
- Machine Learning and Algorithms 3
- Machine Learning and ELM 3
- Domain Adaptation and Few-Shot Learning 3
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- Face and Expression Recognition 4
- Cognitive Neuroscience top 10%
- Face Recognition and Perception 3
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- Sparse and Compressive Sensing Techniques 7
- Co-authors
- Tomaso PoggioH. N. MhaskarBrando MirandaLorenzo RosascoJoel Z. LeiboFabio AnselmiWinrich A. FreiwaldVijay Chandrasekhar
- Journals
- Bulletin of the Polish Academy of Sciences Technical Sciences (2 papers)IEEE Transactions on Consumer Electronics (1 paper)Nature Communications (1 paper)
- Partner nations
- United StatesItalyChina
In The Last Decade
Qianli Liao
23 papers receiving 702 citations
Hit Papers
Peers
Comparison fields: 5 of 115
- Artificial Intelligence 337
- Computational Mathematics 6
- Statistical and Nonlinear Physics 109
- Computer Vision and Pattern Recognition 156
- Cognitive Neuroscience 112
Countries citing papers authored by Qianli Liao
This map shows the geographic impact of Qianli Liao's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Qianli Liao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Qianli Liao more than expected).
Fields of papers citing papers by Qianli Liao
This network shows the impact of papers produced by Qianli Liao. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Qianli Liao. The network helps show where Qianli Liao may publish in the future.
Co-authorship network
The 23 scholars most cited alongside Qianli Liao, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2023 | 9 | |
| 3 | 2020 | 14 | |
| 4 | Implicit dynamic regularization in deep networks | 2020 | 0 |
| 5 | 2020 | 84 | |
| 6 | Theory III: Dynamics and Generalization in Deep Networks -- a simple solution | 2019 | 2 |
| 7 | 2018 | 5 | |
| 8 | Biologically-Plausible Learning Algorithms Can Scale to Large Datasets | 2018 | 0 |
| 9 | 2018 | 14 | |
| 10 | When Is Handcrafting Not a Curse | 2018 | 1 |
| 11 | 2017 | 108 | |
| 12 | 2016 | 5 | |
| 13 | Why and When Can Deep -- but Not Shallow -- Networks Avoid the Curse of Dimensionality | 2016 | 3 |
| 14 | 2016 | 34 | |
| 15 | Learning Real and Boolean Functions: When Is Deep Better Than Shallow | 2016 | 20 |
| 16 | 2016 | 50 | |
| 17 | 2016 | 3 | |
| 18 | 2015 | 23 | |
| 19 | Subtasks of Unconstrained Face Recognition | 2014 | 9 |
| 20 | Learning invariant representations and applications to face verification | 2013 | 19 |
About Qianli Liao
Qianli Liao is a scholar working on Computational Mathematics, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 26 papers that have together received 725 indexed citations. Recurring topics across this work include Neural Networks and Applications (9 papers), Sparse and Compressive Sensing Techniques (7 papers), Stochastic Gradient Optimization Techniques (7 papers), Face and Expression Recognition (4 papers), Face Recognition and Perception (3 papers), Machine Learning and Algorithms (3 papers), Machine Learning and ELM (3 papers) and Domain Adaptation and Few-Shot Learning (3 papers). The work is most often cited by research in Artificial Intelligence (337 citations), Computational Mathematics (6 citations) and Statistical and Nonlinear Physics (109 citations). Qianli Liao has collaborated with scholars based in United States, Italy and China. Frequent co-authors include Tomaso Poggio, H. N. Mhaskar, Brando Miranda, Lorenzo Rosasco, Joel Z. Leibo, Fabio Anselmi, Winrich A. Freiwald, Vijay Chandrasekhar, Mengjia Xu and Jie Lin. Their work appears in journals such as Bulletin of the Polish Academy of Sciences Technical Sciences, IEEE Transactions on Consumer Electronics, Nature Communications, Current Biology and PLoS Computational Biology.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.