Hung Hai Bui

Artificial Intelligence · 1996–2019

A researcher in the Science corpus: 10 papers; trusts 92 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 85 citations into it; #9,819 of 35,345 by trust here, #5,924 by citations
• Computer Vision and Pattern Recognition — 50 citations into it; #9,870 of 21,575 by trust here, #8,831 by citations
• Reinforcement Learning — 30 citations into it
• Generative Models — 25 citations into it
• Probabilistic & Bayesian Methods — 25 citations into it
• Representation & Self-Supervised Learning — 25 citations into it; #2,029 of 3,145 by trust here, #1,191 by citations
• Optimization & Training — 15 citations into it
• Management Science and Operations Research — 10 citations into it

Whom Hung Hai Bui trusts overall

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

1. Diederik P. Kingma
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Ruslan Salakhutdinov
5. Alex Krizhevsky
6. Yann LeCun
7. Michael I. Jordan
8. Radford M. Neal
9. Max Welling
10. Tommi S. Jaakkola

Who trusts Hung Hai Bui overall

1. Siamak Ravanbakhsh
2. Qingyun Wu
3. Yanchao Yang
4. Seyed Mehran Kazemi
5. Jian Liang
6. Yu Kong
7. Michalis Raptis
8. M. I. Oluwatoyin P. Popoola
9. Jeff Schneider
10. Gal Lavee

Papers from Hung Hai Bui's perspective

Ordered by how strongly the researchers Hung Hai Bui 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. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. GENERATIVE ADVERSARIAL NETS
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• A DIRT-T Approach to Unsupervised Domain Adaptation (2018) — 665 citations
• Robust Locally-Linear Controllable Embedding (2017) — 101 citations
• Activity recognition and abnormality detection with the switching hidden semi-Markov model (2005) — 613 citations
• Amortized Inference Regularization (2018) — 89 citations
• Learning and detecting activities from movement trajectories using the hierarchical hidden Markov model (2005) — 374 citations
• Prediction, Consistency, Curvature: Representation Learning for Locally-Linear Control (2019) — 35 citations
• Large-scale Online Kernel Learning with Random Feature Reparameterization (2017) — 51 citations
• Efficient Thompson Sampling for Online Matrix-Factorization Recommendation (2015) — 196 citations
• Automorphism Groups of Graphical Models and Lifted Variational Inference (2012) — 74 citations
• Learning other Agents' Preferences in Multiagent Negotiation (1996)