Rob Brekelmans

Artificial Intelligence · 2018

A researcher in the Science corpus: 4 papers; trusts 61 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 45 citations into it; #16,026 of 35,345 by trust here, #15,542 by citations
• Computer Vision and Pattern Recognition — 28 citations into it; #20,824 of 21,575 by trust here, #14,151 by citations
• Representation & Self-Supervised Learning — 24 citations into it; #3,018 of 3,145 by trust here, #2,404 by citations
• Generative Models — 17 citations into it
• Safety, Robustness & Interpretability — 9 citations into it; #2,132 of 2,251 by trust here, #1,780 by citations
• Information Theory — 6 citations into it
• Computer Networks and Communications — 5 citations into it
• Probabilistic & Bayesian Methods — 5 citations into it

Whom Rob Brekelmans trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Alessandro Achille
4. Yoshua Bengio
5. J. Urgen Schmidhuber
6. Alex Krizhevsky
7. Max Welling
8. Yann LeCun
9. Stefano Soatto
10. Radford M. Neal

Who trusts Rob Brekelmans overall

1. Fei Ye
2. Adrian G. Bors
3. Emilien Dupont
4. Congzheng Song
5. Adar Elad
6. Babak Esmaeili

Papers from Rob Brekelmans's perspective

Ordered by how strongly the researchers Rob Brekelmans 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. GENERATIVE ADVERSARIAL NETS
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Invariant Representations without Adversarial Training (2018) — 250 citations
• Auto-Encoding Total Correlation Explanation (2018) — 84 citations
• Evading the Adversary in Invariant Representation (2018) — 7 citations
• P4ML: A Phased Performance-Based Pipeline Planner for Automated Machine Learning (2018)