Truyen Tran

Artificial Intelligence · 2010–2020

A researcher in the Science corpus: 15 papers; trusts 990 researchers and is trusted by 24 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 231 citations into it; #8,037 of 35,345 by trust here, #4,797 by citations
• Computer Vision and Pattern Recognition — 195 citations into it; #14,891 of 21,575 by trust here, #7,657 by citations
• Model Architectures — 58 citations into it
• Representation & Self-Supervised Learning — 54 citations into it
• Generative Models — 48 citations into it
• Optimization & Training — 46 citations into it
• Question Answering & Reading Comprehension — 38 citations into it; #177 of 335 by trust here, #150 by citations
• Natural Language Processing — 34 citations into it

Whom Truyen Tran trusts overall

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

1. Geoffrey E. Hinton
2. J. Urgen Schmidhuber
3. Alex Krizhevsky
4. Diederik P. Kingma
5. Yoshua Bengio
6. Yann LeCun
7. Kaiming He
8. Alex Graves
9. Ilya Sutskever
10. Sepp Hochreiter

Who trusts Truyen Tran overall

1. Aleksandar Bojchevski
2. Stephan Günnemann
3. Rafael-Michael Karampatsis
4. Yuan Luo
5. Benjamin Shickel
6. Daniel Zügner
7. Ying Sha
8. Johannes Klicpera
9. William L. Hamilton
10. Katsuhiko Ishiguro

Papers from Truyen Tran's perspective

Ordered by how strongly the researchers Truyen Tran 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Hierarchical Conditional Relation Networks for Video Question Answering (2020) — 301 citations
• On catastrophic forgetting and mode collapse in Generative Adversarial Networks (2018) — 64 citations
• Catastrophic forgetting and mode collapse in GANs (2020) — 283 citations
• Column Networks for Collective Classification (2016) — 152 citations
• A deep language model for software code (2016) — 112 citations
• Hierarchical Conditional Relation Networks for Multimodal Video Question Answering (2020) — 30 citations
• Nonnegative shared subspace learning and its application to social media retrieval (2010) — 105 citations
• Graph Classification via Deep Learning with Virtual Nodes (2017) — 57 citations
• Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View (2016) — 874 citations
• Neural Reasoning, Fast and Slow, for Video Question Answering (2019) — 14 citations
• Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videos (2019) — 321 citations
• Theory and Evaluation Metrics for Learning Disentangled Representations (2019) — 109 citations
• Predicting healthcare trajectories from medical records: A deep learning approach (2017) — 393 citations
• $\mathtt {Deepr}$: A Convolutional Net for Medical Records (2016) — 400 citations
• DeepCare: A Deep Dynamic Memory Model for Predictive Medicine (2016) — 304 citations