Zachary Nado

3.2k citations
3 papers · 102 indexed · h-index 3
Topics
Software Engineering Research (1 paper)Parallel Computing and Optimization Techniques (1 paper)Anomaly Detection Techniques and Applications (1 paper)
Journals
arXiv (Cornell University)International Conference on Learning Representations
Partner nations
United StatesCanada

In The Last Decade

Zachary Nado

3 papers receiving 94 citations

Peers

Zachary Nado
Comparison fields: 5 of 46
  • Artificial Intelligence 74
  • Computer Vision and Pattern Recognition 33
  • Molecular Biology 6
  • Signal Processing 6
  • Computer Networks and Communications 6
Replace Yusuke Naito with:
Yusuke Naito Japan
Joern-Henrik Jacobsen Canada
Alexandre Bérard France
Xavier Bonnetain France
Patrick Pletscher Switzerland
Xiaohan Chen United States
Chinnadhurai Sankar United States
Lukas Schott Germany
Mohammad Mahdi Kamani United States
Yonatan Geifman Israel
Zachary Nado relative to Yusuke Naito Japan Yusuke Naito's profile →
Citations per field
00.5×3.9×
Yusuke Naito · 1×
Citations per year

Countries citing papers authored by Zachary Nado

Since Specialization
Citations

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

Fields of papers citing papers by Zachary Nado

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Zachary Nado

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

All Works

3 of 3 papers shown
#WorkIndexed citations
1
Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift
92
2
AutoGraph: Imperative-style Coding with Graph-based Performance
6
3
STOCHASTIC GRADIENT LANGEVIN DYNAMICS THAT EXPLOIT NEURAL NETWORK STRUCTURE
4

About Zachary Nado

Zachary Nado is a scholar working on Hardware and Architecture, Information Systems and Artificial Intelligence, having authored 3 papers that have together received 102 indexed citations. Recurring topics across this work include Software Engineering Research (1 paper), Parallel Computing and Optimization Techniques (1 paper) and Anomaly Detection Techniques and Applications (1 paper). The work is most often cited by research in Artificial Intelligence (74 citations), Computer Vision and Pattern Recognition (33 citations) and Health Informatics (1 citation). Zachary Nado has collaborated with scholars based in United States and Canada. Frequent co-authors include D. Sculley, Jasper Snoek, Sebastian Nowozin, Joshua V. Dillon, Jie Ren, Yaniv Ovadia, Balaji Lakshminarayanan, Emily Fertig, Fei Wang and Dan Moldovan. Their work appears in journals such as 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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