Daniel Guo

505 total citations
3 papers, 134 citations indexed

About

Daniel Guo is a scholar working on Artificial Intelligence, Mathematical Physics and Computer Vision and Pattern Recognition. According to data from OpenAlex, Daniel Guo has authored 3 papers receiving a total of 134 indexed citations (citations by other indexed papers that have themselves been cited), including 2 papers in Artificial Intelligence, 1 paper in Mathematical Physics and 1 paper in Computer Vision and Pattern Recognition. Recurrent topics in Daniel Guo's work include Sparse and Compressive Sensing Techniques (1 paper), Speech and dialogue systems (1 paper) and Mathematical Analysis and Transform Methods (1 paper). Daniel Guo is often cited by papers focused on Sparse and Compressive Sensing Techniques (1 paper), Speech and dialogue systems (1 paper) and Mathematical Analysis and Transform Methods (1 paper). Daniel Guo collaborates with scholars based in United States, United Kingdom and Sweden. Daniel Guo's co-authors include Wen-tau Yih, Geoffrey Zweig, Gökhan Tür, Ashkan Panahi, Christos Thrampoulidis, Babak Hassibi, Pablo Sprechmann, Charles Blundell, Steven Kapturowski and Martín Arjovsky and has published in prestigious journals such as arXiv (Cornell University) and Chalmers Research (Chalmers University of Technology).

In The Last Decade

Daniel Guo

3 papers receiving 125 citations

Peers

Daniel Guo

AI

CVPR

CM

SP

CNC

Prafulla Dhariwal

Jaehong Yoon South Korea

Anastasia Pentina Austria

Yinchong Yang Germany

Francesco Croce Germany

Patrick Pletscher Switzerland

Tuan Dung Nguyen Australia

Nicolas Keriven France

Tongzheng Ren United States

Jongwook Choi South Korea

Prafulla Dhariwal

Daniel Guo

92 ×0.8

AI

54 ×2.1

CVPR

4 ×0.3

CM

16 ×1.3

SP

5 ×0.6

CNC

Citations per year, relative to Daniel Guo Daniel Guo (= 1×) peers Prafulla Dhariwal

Countries citing papers authored by Daniel Guo

Since Specialization

Citations

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

Fields of papers citing papers by Daniel Guo

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel Guo

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Guo. A scholar is included among the top collaborators of Daniel Guo 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 Daniel Guo. Daniel Guo is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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3 of 3 papers shown

1.

Badia, Adrià Puigdomènech, Pablo Sprechmann, Daniel Guo, et al.. (2020). Never Give Up: Learning Directed Exploration Strategies. arXiv (Cornell University). 27 indexed citations

2.

Thrampoulidis, Christos, Ashkan Panahi, Daniel Guo, & Babak Hassibi. (2015). Precise error analysis of the LASSO. Chalmers Research (Chalmers University of Technology). 28. 3467–3471. 15 indexed citations

3.

Guo, Daniel, Gökhan Tür, Wen-tau Yih, & Geoffrey Zweig. (2014). Joint semantic utterance classification and slot filling with recursive neural networks. 554–559. 92 indexed citations

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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