Tri Dao
Impact in
Papers in
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- Multimodal Machine Learning Applications 2
- Advanced Image and Video Retrieval Techniques 2
- Advanced Neural Network Applications 2
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- Domain Adaptation and Few-Shot Learning 1
- Bayesian Modeling and Causal Inference 1
- Co-authors
- Albert Gu (4 shared papers)Atri Rudra (2 shared papers)Christopher De (2 shared papers)Cristina Re (2 shared papers)Christopher Ré (4 shared papers)Beidi Chen (2 shared papers)Alexander Ratner (1 shared paper)Aaron Gokaslan (1 shared paper)
- Journals
- PubMed (5 papers)International Conference on Learning Representations (1 paper)neural information processing systems (1 paper)arXiv (Cornell University) (2 papers)Uncertainty in Artificial Intelligence (1 paper)
- Partner nations
- United StatesUnited KingdomDenmark
In The Last Decade
Tri Dao
10 papers receiving 77 citations
Peers
Comparison fields: 5 of 44
- Health Informatics 3
- Computational Mathematics 1
- Artificial Intelligence 36
- Computer Vision and Pattern Recognition 19
- Signal Processing 4
Countries citing papers authored by Tri Dao
This map shows the geographic impact of Tri Dao'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 Tri Dao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tri Dao more than expected).
Fields of papers citing papers by Tri Dao
This network shows the impact of papers produced by Tri Dao. 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 Tri Dao. The network helps show where Tri Dao may publish in the future.
Co-authors
The 25 scholars most cited alongside Tri Dao, 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 Kernel Theory of Modern Data Augmentation. | 2019 | 19 |
| 2 | 2024 | 19 | |
| 3 | Scatterbrain: Unifying Sparse and Low-rank Attention | 2021 | 14 |
| 4 | Learning Fast Algorithms for Linear Transforms Using Butterfly Factorizations. | 2019 | 7 |
| 5 | On the Downstream Performance of Compressed Word Embeddings. | 2019 | 6 |
| 6 | MONGOOSE: A Learnable LSH Framework for Efficient Neural Network Training | 2021 | 5 |
| 7 | Gaussian Quadrature for Kernel Features. | 2017 | 3 |
| 8 | 2024 | 3 | |
| 9 | Adaptive Hashing for Model Counting. | 2019 | 2 |
| 10 | 2019 | 1 | |
| 11 | 2024 | 0 | |
| 12 | Catformer: Designing Stable Transformers via Sensitivity Analysis | 2021 | 0 |
About Tri Dao
Tri Dao is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Signal Processing, Molecular Biology and Control and Systems Engineering, having authored 12 papers that have together received 79 indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (2 papers), Advanced Image and Video Retrieval Techniques (2 papers), Advanced Neural Network Applications (2 papers), Domain Adaptation and Few-Shot Learning (1 paper), Magnetic Bearings and Levitation Dynamics (1 paper), Bayesian Modeling and Causal Inference (1 paper), Markov Chains and Monte Carlo Methods (1 paper) and Vibration and Dynamic Analysis (1 paper). The work is most often cited by research in Health Informatics (3 citations), Computational Mathematics (1 citation), Artificial Intelligence (36 citations), Computer Vision and Pattern Recognition (19 citations) and Signal Processing (4 citations). Tri Dao has collaborated with scholars based in United States, United Kingdom and Denmark. Frequent co-authors include Albert Gu, Atri Rudra, Christopher De, Cristina Re, Christopher Ré, Beidi Chen, Alexander Ratner, Aaron Gokaslan, Volodymyr Kuleshov and Christopher Ré. Their work appears in journals such as PubMed, International Conference on Learning Representations, neural information processing systems, arXiv (Cornell University) and Uncertainty in 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.