Maxinder S Kanwal
- 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
- David KruegerAaron CourvilleDevansh ArpitStanisław JastrzȩbskiNicolas BallasTegan MaharajAsja FischerEmmanuel Bengio
- Topics
- Stochastic Gradient Optimization Techniques (2 papers)Advanced Graph Neural Networks (1 paper)Privacy-Preserving Technologies in Data (1 paper)
- Cited by
- Artificial IntelligenceComputer Vision and Pattern RecognitionIndustrial and Manufacturing Engineering
- Journals
- PolyPublie (École Polytechnique de Montréal)Jagiellonian University Repository (Jagiellonian University)
In The Last Decade
Maxinder S Kanwal
2 papers receiving 358 citations
Hit Papers
Peers
Comparison fields: 5 of 65
- Artificial Intelligence 292
- Computer Vision and Pattern Recognition 157
- Civil and Structural Engineering 25
- Industrial and Manufacturing Engineering 20
- Radiology, Nuclear Medicine and Imaging 18
Countries citing papers authored by Maxinder S Kanwal
This map shows the geographic impact of Maxinder S Kanwal'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 Maxinder S Kanwal with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maxinder S Kanwal more than expected).
Fields of papers citing papers by Maxinder S Kanwal
This network shows the impact of papers produced by Maxinder S Kanwal. 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 Maxinder S Kanwal. The network helps show where Maxinder S Kanwal may publish in the future.
Co-authorship network of co-authors of Maxinder S Kanwal
This figure shows the co-authorship network connecting the top 25 collaborators of Maxinder S Kanwal. A scholar is included among the top collaborators of Maxinder S Kanwal 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 Maxinder S Kanwal. Maxinder S Kanwal is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Deep Nets Don't Learn via Memorization | 23 |
| 2 | A closer look at memorization in deep networksbreakdown → | 344 |
About Maxinder S Kanwal
Maxinder S Kanwal is a scholar working on Artificial Intelligence, Infectious Diseases and Organic Chemistry, having authored 2 papers that have together received 367 indexed citations. Recurring topics across this work include Stochastic Gradient Optimization Techniques (2 papers), Advanced Graph Neural Networks (1 paper) and Privacy-Preserving Technologies in Data (1 paper). The work is most often cited by research in Artificial Intelligence (292 citations), Computer Vision and Pattern Recognition (157 citations) and Industrial and Manufacturing Engineering (20 citations). Maxinder S Kanwal has collaborated with scholars based in Poland, Canada and Germany. Frequent co-authors include David Krueger, Aaron Courville, Devansh Arpit, Stanisław Jastrzȩbski, Nicolas Ballas, Tegan Maharaj, Asja Fischer, Emmanuel Bengio, Simon Lacoste-Julien and Yoshua Bengio. Their work appears in journals such as PolyPublie (École Polytechnique de Montréal) and Jagiellonian University Repository (Jagiellonian 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.