Weijie Su

112 papers receiving 2.8k citations

Hit Papers

Intern VL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks 2024 · 105 citations
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Peers

Weijie Su
Comparison fields: 5 of 180
  • Statistics and Probability 256
  • Numerical Analysis 117
  • Biomaterials 284
  • Rehabilitation 139
  • Computational Mathematics 11
Replace Jing Lei with:
Jing Lei China
Guoan Chen China
R. Sridhar India
Guofeng Zhang China
Shuqiang Wang China
Sivaraman Balakrishnan United States
Yu Zhu China
Lihua Li China
Yuanyuan Liu China
Yang Wang China
Weijie Su relative to Jing Lei China Jing Lei's profile →
Citations per field
00.5×10×14.6×
Jing Lei · 1×
Citations per year

Countries citing papers authored by Weijie Su

Since Specialization
Citations

This map shows the geographic impact of Weijie Su'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 Weijie Su with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Weijie Su more than expected).

Fields of papers citing papers by Weijie Su

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Weijie Su. 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 Weijie Su. The network helps show where Weijie Su may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Weijie Su, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Weijie Su Line = papers co-authored together Weijie Su links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
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VL-BERT: Pre-training of Generic Visual-Linguistic Representations
2020112
16
Label-Aware Neural Tangent Kernel: Toward Better Generalization and Local Elasticity
20201
17
The Local Elasticity of Neural Networks
20203
18
Acceleration via Symplectic Discretization of High-Resolution Differential Equations
20192
19
Detecting Replicating Signals using Adaptive Filtering Procedures with the Application in High-throughput Experiments
20163
20
A Differential Equation for Modeling Nesterov’s Accelerated Gradient Method: Theory and Insights
201468

About Weijie Su

Weijie Su is a scholar working on Acoustics and Ultrasonics, Statistics and Probability, Dermatology, Numerical Analysis and Artificial Intelligence, having authored 126 papers that have together received 2.8k indexed citations. Recurring topics across this work include Statistical Methods and Inference (11 papers), Dermatologic Treatments and Research (8 papers), Stochastic Gradient Optimization Techniques (8 papers), Sparse and Compressive Sensing Techniques (7 papers), Porphyrin and Phthalocyanine Chemistry (7 papers), Privacy-Preserving Technologies in Data (7 papers), Injection Molding Process and Properties (7 papers) and Nonlinear Optical Materials Studies (7 papers). The work is most often cited by research in Statistics and Probability (256 citations), Numerical Analysis (117 citations), Biomaterials (284 citations), Rehabilitation (139 citations) and Computational Mathematics (11 citations). Weijie Su has collaborated with scholars based in United States, China and Taiwan. Frequent co-authors include Emmanuel J. Candès, Stephen Boyd, Thomas M. Cooper, Małgorzata Bogdan, Timothy J. Bunning, Mark C. Brant, Hao Jiang, Xizhou Zhu, Chiara Sabatti and Yi Xin Zhang. Their work appears in journals such as Journal of the American Statistical Association, The Annals of Statistics, Journal of the Royal Statistical Society Series B (Statistical Methodology), Advanced Healthcare Materials and Polymer.

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.

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