Rudy Bunel

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 6 papers; trusts 107 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 115 citations into it; #19,400 of 35,345 by trust here, #8,706 by citations
• Computer Vision and Pattern Recognition — 55 citations into it; #20,757 of 21,575 by trust here, #14,151 by citations
• Safety, Robustness & Interpretability — 55 citations into it
• Natural Language Processing — 21 citations into it; #2,321 of 5,688 by trust here, #2,567 by citations
• Electrical and Electronic Engineering — 16 citations into it; #10,485 of 16,539 by trust here, #7,640 by citations
• Model Architectures — 16 citations into it
• Program Analysis & Verification — 15 citations into it
• Signal Processing — 12 citations into it

Whom Rudy Bunel trusts overall

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

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

Who trusts Rudy Bunel overall

1. Xinyun Chen
2. Vincent Tjeng
3. Krishnamurthy (Dj) Dvijotham
4. Richard Shin
5. Jorge Armando Mendez Mendez
6. Hossein Hajipour
7. Sven Gowal
8. Sungyoon Lee
9. Robert Stanforth
10. Shao-Hua Sun

Papers from Rudy Bunel's perspective

Ordered by how strongly the researchers Rudy Bunel 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. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
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

• Neural Program Meta-Induction (2017) — 74 citations
• On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models (2018) — 640 citations
• Contrastive Training for Improved Out-of-Distribution Detection (2020) — 271 citations
• Piecewise Linear Neural Network verification: A comparative study (2017) — 73 citations
• Leveraging Grammar and Reinforcement Learning for Neural Program Synthesis (2018) — 231 citations
• A Unified View of Piecewise Linear Neural Network Verification (2017) — 369 citations