Mary Phuong

1.3k citations
6 papers · 159 indexed · h-index 4
Topics
Neural Networks and Applications (2 papers)Machine Learning and Data Classification (2 papers)Model Reduction and Neural Networks (2 papers)
Journals
Measurement Science ReviewarXiv (Cornell University)International Conference on Learning Representations

In The Last Decade

Mary Phuong

5 papers receiving 158 citations

Peers

Mary Phuong
Comparison fields: 5 of 47
  • Artificial Intelligence 113
  • Computer Vision and Pattern Recognition 78
  • Computer Networks and Communications 16
  • Electrical and Electronic Engineering 12
  • Signal Processing 9
Replace Suraj Srinivas with:
Suraj Srinivas India
Jiefeng Peng China
Danlu Chen United States
Jaehyung Kim South Korea
Huangjie Zheng China
Yen-Chang Hsu United States
Łukasz Dudziak United Kingdom
Chaim Baskin Israel
Romaric Audigier France
Mary Phuong relative to Suraj Srinivas India Suraj Srinivas's profile →
Citations per field
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Suraj Srinivas · 1×
Citations per year

Countries citing papers authored by Mary Phuong

Since Specialization
Citations

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

Fields of papers citing papers by Mary Phuong

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mary Phuong

This figure shows the co-authorship network connecting the top 25 collaborators of Mary Phuong. A scholar is included among the top collaborators of Mary Phuong based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Mary Phuong. Mary Phuong is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

6 of 6 papers shown
#WorkIndexed citations
1 1
2
The inductive bias of ReLU networks on orthogonally separable data
0
3 46
4
Functional vs. parametric equivalence of ReLU networks
6
5 100
6
The Mutual Autoencoder: Controlling Information in Latent Code Representations
6

About Mary Phuong

Mary Phuong is a scholar working on Statistical and Nonlinear Physics, Electrochemistry and Artificial Intelligence, having authored 6 papers that have together received 159 indexed citations. Recurring topics across this work include Neural Networks and Applications (2 papers), Machine Learning and Data Classification (2 papers) and Model Reduction and Neural Networks (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (78 citations), Artificial Intelligence (113 citations) and Signal Processing (9 citations). Mary Phuong has collaborated with scholars based in Austria, Slovakia and Netherlands. Frequent co-authors include Christoph H. Lampert, Nate Kushman, Max Welling, Ryota Tomioka, Sebastian Nowozin and Anna Krakovská. Their work appears in journals such as Measurement Science Review, arXiv (Cornell University) and International Conference on Learning Representations.

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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