Brandon Tran

Artificial Intelligence · 2017–2019

A researcher in the Science corpus: 7 papers; trusts 337 researchers and is trusted by 33 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 188 citations into it; #7,044 of 35,345 by trust here, #4,888 by citations
• Computer Vision and Pattern Recognition — 184 citations into it; #12,538 of 21,575 by trust here, #3,459 by citations
• Safety, Robustness & Interpretability — 136 citations into it; #434 of 2,251 by trust here, #278 by citations
• Generative Models — 76 citations into it
• Signal Processing — 34 citations into it; #1,173 of 7,572 by trust here, #519 by citations
• Model Architectures — 20 citations into it
• Optimization & Training — 18 citations into it
• Representation & Self-Supervised Learning — 17 citations into it; #2,936 of 3,145 by trust here, #2,805 by citations

Whom Brandon Tran trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Geoffrey E. Hinton
4. Karen Simonyan
5. Diederik P. Kingma
6. Ian J. Goodfellow
7. Yoshua Bengio
8. Alex Graves
9. Yann LeCun
10. Christian Szegedy

Who trusts Brandon Tran overall

1. Eugene Bagdasarian
2. Yige Li
3. Dongxian Wu
4. Virat Shejwalkar
5. Goutham Ramakrishnan
6. T. Tan
7. Yanyao Shen
8. Lingjuan Lyu
9. Alexander Turner
10. Xingjun Ma

Papers from Brandon Tran's perspective

Ordered by how strongly the researchers Brandon 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
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. Going deeper with convolutions

Their papers

• Adversarial Examples Are Not Bugs, They Are Features (2019) — 2,151 citations
• Exploring the Landscape of Spatial Robustness (2017) — 564 citations
• Computer Vision with a Single (Robust) Classifier (2019) — 36 citations
• Learning Perceptually-Aligned Representations via Adversarial Robustness (2019) — 63 citations
• Spectral Signatures in Backdoor Attacks (2018) — 1,009 citations
• Adversarial Robustness as a Prior for Learned Representations (2019) — 240 citations
• Image Synthesis with a Single (Robust) Classifier (2019) — 217 citations