Harm de Vries
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- Multimodal Machine Learning Applications 7
- Advanced Image and Video Retrieval Techniques 2
- Artificial Intelligence top 2%
- Topic Modeling 4
- Domain Adaptation and Few-Shot Learning 3
- Neural Networks and Applications 2
- Speech and dialogue systems 2
- Stochastic Gradient Optimization Techniques 2
- Signal Processing top 5%
- Media Technology top 10%
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- Model Reduction and Neural Networks 1
- Co-authors
- Aaron CourvilleFlorian StrubVincent DumoulinEthan PerezYoshua BengioYann DauphinNathan SchucherSiva Reddy
- Journals
- Transactions of the Association for Computational Linguistics (1 paper)Topology and its Applications (1 paper)Intelligent Data Analysis (1 paper)
- Partner nations
- CanadaUnited KingdomUnited States
In The Last Decade
Harm de Vries
13 papers receiving 1.1k citations
Hit Papers
Peers
Comparison fields: 5 of 106
- Computer Vision and Pattern Recognition 637
- Artificial Intelligence 565
- Signal Processing 162
- Computer Graphics and Computer-Aided Design 30
- Media Technology 33
Countries citing papers authored by Harm de Vries
This map shows the geographic impact of Harm de Vries'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 Harm de Vries with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Harm de Vries more than expected).
Fields of papers citing papers by Harm de Vries
This network shows the impact of papers produced by Harm de Vries. 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 Harm de Vries. The network helps show where Harm de Vries may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Harm de Vries, 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 | 2024 | 1 | |
| 2 | 2023 | 1 | |
| 3 | 2022 | 41 | |
| 4 | 2021 | 16 | |
| 5 | 2020 | 2 | |
| 6 | 2020 | 2 | |
| 7 | Talk The Walk: Navigating Grids in New York City through Grounded Dialogue | 2018 | 2 |
| 8 | 2018 | 78 | |
| 9 | FiLM: Visual Reasoning with a General Conditioning Layerbreakdown → | 2018 | 788 |
| 10 | 2017 | 36 | |
| 11 | 2017 | 6 | |
| 12 | Deep Learning Vector Quantization. | 2016 | 12 |
| 13 | 2016 | 1 | |
| 14 | 2015 | 119 |
About Harm de Vries
Harm de Vries is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Algebra and Number Theory, Signal Processing and Computer Science Applications, having authored 14 papers that have together received 1.1k indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (7 papers), Topic Modeling (4 papers), Domain Adaptation and Few-Shot Learning (3 papers), Neural Networks and Applications (2 papers), Speech and dialogue systems (2 papers), Advanced Image and Video Retrieval Techniques (2 papers), Stochastic Gradient Optimization Techniques (2 papers) and Model Reduction and Neural Networks (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (637 citations), Artificial Intelligence (565 citations), Signal Processing (162 citations), Computer Graphics and Computer-Aided Design (30 citations) and Media Technology (33 citations). Harm de Vries has collaborated with scholars based in Canada, United Kingdom and United States. Frequent co-authors include Aaron Courville, Florian Strub, Vincent Dumoulin, Ethan Perez, Yoshua Bengio, Yann Dauphin, Nathan Schucher, Siva Reddy, Kaheer Suleman and Shehzaad Dhuliawala. Their work appears in journals such as Transactions of the Association for Computational Linguistics, Topology and its Applications, Intelligent Data Analysis, University of Groningen research database (University of Groningen / Centre for Information Technology) and Proceedings of the AAAI Conference on Artificial Intelligence.
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.