Dinh Q. Phung

Artificial Intelligence · 2005–2018

A researcher in the Science corpus: 16 papers; trusts 425 researchers and is trusted by 45 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 161 citations into it; #7,113 of 35,345 by trust here, #5,458 by citations
• Computer Vision and Pattern Recognition — 114 citations into it; #5,387 of 21,575 by trust here, #5,205 by citations
• Generative Models — 54 citations into it; #1,707 of 1,956 by trust here, #1,382 by citations
• Model Architectures — 36 citations into it
• Probabilistic & Bayesian Methods — 33 citations into it; #929 of 1,525 by trust here, #1,176 by citations
• Optimization & Training — 26 citations into it; #2,417 of 2,601 by trust here, #1,661 by citations
• Representation & Self-Supervised Learning — 26 citations into it
• Natural Language Processing — 25 citations into it

Whom Dinh Q. Phung trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. B. Vo
4. Radford M. Neal
5. Yoshua Bengio
6. Diederik P. Kingma
7. Alex Graves
8. Yann LeCun
9. Ilya Sutskever
10. J. Urgen Schmidhuber

Who trusts Dinh Q. Phung overall

1. Jason L. Williams
2. Seyed Hamid Rezatofighi
3. B. Vo
4. Aleksandar Bojchevski
5. Karl Granström
6. Ngoc-Trung Tran
7. Stephan Günnemann
8. Kostadin Georgiev
9. Yuan Luo
10. Sarvesh Vishwakarma

Papers from Dinh Q. Phung's perspective

Ordered by how strongly the researchers Dinh Q. Phung trusts endorse each paper. Same corpus, a different order for every researcher.

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

Their papers

• Activity recognition and abnormality detection with the switching hidden semi-Markov model (2005) — 613 citations
• Dual Discriminator Generative Adversarial Nets (2017) — 346 citations
• Column Networks for Collective Classification (2016) — 152 citations
• Model-based classification and novelty detection for point pattern data (2016) — 14 citations
• Model-Based Multiple Instance Learning (2017) — 5 citations
• Learning and detecting activities from movement trajectories using the hierarchical hidden Markov model (2005) — 374 citations
• Clustering for point pattern data (2017) — 13 citations
• Multi-Generator Generative Adversarial Nets (2017) — 69 citations
• Nonnegative shared subspace learning and its application to social media retrieval (2010) — 105 citations
• MGAN: Training Generative Adversarial Nets with Multiple Generators (2018) — 250 citations
• Large-scale Online Kernel Learning with Random Feature Reparameterization (2017) — 51 citations
• Predicting healthcare trajectories from medical records: A deep learning approach (2017) — 393 citations
• A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network (2017) — 829 citations
• DeepCare: A Deep Dynamic Memory Model for Predictive Medicine (2016) — 304 citations
• Labeled Random Finite Sets and the Bayes Multi-Target Tracking Filter (2013) — 705 citations
• Ordinal Boltzmann Machines for Collaborative Filtering (2009) — 72 citations