Dzung T. Phan

Artificial Intelligence · 2014–2020

A researcher in the Science corpus: 4 papers; trusts 41 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 44 citations into it; #34,843 of 35,345 by trust here, #23,731 by citations
• Optimization & Training — 37 citations into it
• Computational Mechanics — 25 citations into it; #9,847 of 10,001 by trust here, #6,877 by citations
• Statistics and Probability — 13 citations into it
• Computational Theory and Mathematics — 10 citations into it
• Management Science and Operations Research — 9 citations into it
• Statistical Learning Theory — 7 citations into it
• Computer Vision and Pattern Recognition — 6 citations into it

Whom Dzung T. Phan trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Christodoulos A. Floudas
2. L. Eon Bottou
3. B. H. Kim
4. Yurii E. Nesterov
5. Chih-Jen Lin
6. François Bouffard
7. Alex Krizhevsky
8. Dimitri P. Bertsekas
9. Sicco Verwer
10. Aaron Defazio

Who trusts Dzung T. Phan overall

1. Peter Richtárik

Papers from Dzung T. Phan's perspective

Ordered by how strongly the researchers Dzung T. Phan trusts endorse each paper. Same corpus, a different order for every researcher.

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Gradient-based learning applied to document recognition
4. ImageNet classification with deep convolutional neural networks
5. Learning Multiple Layers of Features from Tiny Images
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Hybrid Stochastic Gradient Descent Algorithms for Stochastic Nonconvex Optimization (2019) — 60 citations
• A Unified Convergence Analysis for Shuffling-Type Gradient Methods (2020) — 100 citations
• A Scalable MIP-based Method for Learning Optimal Multivariate Decision Trees (2020) — 46 citations
• Some Efficient Optimization Methods for Solving the Security-Constrained Optimal Power Flow Problem (2014) — 96 citations