Tegan Maharaj
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 5%
- Civil and Structural Engineering
- Industrial and Manufacturing Engineering
- Radiology, Nuclear Medicine and Imaging
- Co-authors
- Aaron CourvilleNicolas BallasDavid KruegerMaxinder S KanwalDevansh ArpitStanisław JastrzȩbskiEmmanuel BengioAsja Fischer
- Topics
- Stochastic Gradient Optimization Techniques (2 papers)Domain Adaptation and Few-Shot Learning (2 papers)Ethics and Social Impacts of AI (2 papers)
- Journals
- ScienceSHILAP Revista de lepidopterologíaarXiv (Cornell University)
- Partner nations
- CanadaUnited StatesGermany
In The Last Decade
Tegan Maharaj
8 papers receiving 446 citations
Hit Papers
Peers
Comparison fields: 5 of 74
- Artificial Intelligence 339
- Computer Vision and Pattern Recognition 205
- Civil and Structural Engineering 26
- Industrial and Manufacturing Engineering 20
- Radiology, Nuclear Medicine and Imaging 19
Countries citing papers authored by Tegan Maharaj
This map shows the geographic impact of Tegan Maharaj'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 Tegan Maharaj with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tegan Maharaj more than expected).
Fields of papers citing papers by Tegan Maharaj
This network shows the impact of papers produced by Tegan Maharaj. 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 Tegan Maharaj. The network helps show where Tegan Maharaj may publish in the future.
Co-authorship network of co-authors of Tegan Maharaj
This figure shows the co-authorship network connecting the top 25 collaborators of Tegan Maharaj. A scholar is included among the top collaborators of Tegan Maharaj 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 Tegan Maharaj. Tegan Maharaj is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 7 | |
| 4 | 26 | |
| 5 | Deep Nets Don't Learn via Memorization | 23 |
| 6 | ClimateNet: A Machine Learning dataset for Climate Science Research | 1 |
| 7 | Semi-Supervised Detection of Extreme Weather Events in Large Climate Datasets | 7 |
| 8 | Deep Learning for Extreme Weather Detection | 3 |
| 9 | A closer look at memorization in deep networksbreakdown → | 344 |
| 10 | 48 |
About Tegan Maharaj
Tegan Maharaj is a scholar working on Health Informatics, Safety Research and Modeling and Simulation, having authored 10 papers that have together received 459 indexed citations. Recurring topics across this work include Stochastic Gradient Optimization Techniques (2 papers), Domain Adaptation and Few-Shot Learning (2 papers) and Ethics and Social Impacts of AI (2 papers). The work is most often cited by research in Artificial Intelligence (339 citations), Computer Vision and Pattern Recognition (205 citations) and Health Informatics (11 citations). Tegan Maharaj has collaborated with scholars based in Canada, United States and Germany. Frequent co-authors include Aaron Courville, Nicolas Ballas, David Krueger, Maxinder S Kanwal, Devansh Arpit, Stanisław Jastrzȩbski, Emmanuel Bengio, Asja Fischer, Yoshua Bengio and Simon Lacoste-Julien. Their work appears in journals such as Science, SHILAP Revista de lepidopterología and arXiv (Cornell University).
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.