Devansh Arpit
Impact in
-
- Advanced Neural Network Applications
- Artificial Intelligence top 5%
- Machine Learning and Data Classification
- Domain Adaptation and Few-Shot Learning
- Adversarial Robustness in Machine Learning
- Machine Learning and Algorithms
- Anomaly Detection Techniques and Applications
- Topic Modeling
- Neural Networks and Applications
Papers in ⓘ
-
- Stochastic Gradient Optimization Techniques 6
- Adversarial Robustness in Machine Learning 3
- Neural Networks and Applications 2
- Domain Adaptation and Few-Shot Learning 2
- Machine Learning and ELM 1
-
- Model Reduction and Neural Networks 3
- Co-authors
- Aaron Courville (4 shared papers)Yoshua Bengio (5 shared papers)Stanisław Jastrzȩbski (5 shared papers)Emmanuel Bengio (2 shared papers)Tegan Maharaj (2 shared papers)David Krueger (2 shared papers)Asja Fischer (3 shared papers)Maxinder S Kanwal (2 shared papers)
- Journals
- International Conference on Learning Representations (1 paper)Jagiellonian University Repository (Jagiellonian University) (1 paper)arXiv (Cornell University) (6 papers)PolyPublie (École Polytechnique de Montréal) (1 paper)
- Partner nations
- United StatesCanadaGermany
In The Last Decade
Devansh Arpit
12 papers receiving 474 citations
Hit Papers
Peers
Comparison fields: 5 of 77
- Computer Vision and Pattern Recognition 219
- Artificial Intelligence 336
- Statistical and Nonlinear Physics 38
- Signal Processing 32
- Computer Graphics and Computer-Aided Design 8
Countries citing papers authored by Devansh Arpit
This map shows the geographic impact of Devansh Arpit'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 Devansh Arpit with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Devansh Arpit more than expected).
Fields of papers citing papers by Devansh Arpit
This network shows the impact of papers produced by Devansh Arpit. 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 Devansh Arpit. The network helps show where Devansh Arpit may publish in the future.
Co-authors
The 25 scholars most cited alongside Devansh Arpit, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | A closer look at memorization in deep networks Hit paper breakdown → | 2017 | 344 |
| 2 | 2018 | 50 | |
| 3 | Deep Nets Don't Learn via Memorization | 2017 | 23 |
| 4 | On the Spectral Bias of Deep Neural Networks | 2018 | 18 |
| 5 | 2017 | 15 | |
| 6 | 2011 | 11 | |
| 7 | 2018 | 8 | |
| 8 | 2013 | 7 | |
| 9 | 2020 | 4 | |
| 10 | Finding Flatter Minima with SGD | 2018 | 3 |
| 11 | 2020 | 3 | |
| 12 | Joint Training of Deep Auto-Encoders | 2014 | 2 |
| 13 | 2022 | 0 |
About Devansh Arpit
Devansh Arpit is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics, Computer Vision and Pattern Recognition, Signal Processing and Safety Research, having authored 13 papers that have together received 488 indexed citations. Recurring topics across this work include Stochastic Gradient Optimization Techniques (6 papers), Model Reduction and Neural Networks (3 papers), Adversarial Robustness in Machine Learning (3 papers), Neural Networks and Applications (2 papers), Generative Adversarial Networks and Image Synthesis (2 papers), Advanced Neural Network Applications (2 papers), Domain Adaptation and Few-Shot Learning (2 papers) and Machine Learning and ELM (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (219 citations), Artificial Intelligence (336 citations), Statistical and Nonlinear Physics (38 citations), Signal Processing (32 citations) and Computer Graphics and Computer-Aided Design (8 citations). Devansh Arpit has collaborated with scholars based in United States, Canada and Germany. Frequent co-authors include Aaron Courville, Yoshua Bengio, Stanisław Jastrzȩbski, Emmanuel Bengio, Tegan Maharaj, David Krueger, Asja Fischer, Maxinder S Kanwal, Nicolas Ballas and Simon Lacoste-Julien. Their work appears in journals such as International Conference on Learning Representations, Jagiellonian University Repository (Jagiellonian University), arXiv (Cornell University) and PolyPublie (École Polytechnique de Montréal).
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.