Peter O'Connor

Artificial Intelligence · with Löwe · 2013–2019

A researcher in the Science corpus: 3 papers; trusts 229 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 71 citations into it; #17,571 of 35,345 by trust here, #25,040 by citations
• Computer Vision and Pattern Recognition — 47 citations into it
• Representation & Self-Supervised Learning — 33 citations into it; #1,844 of 3,145 by trust here, #2,624 by citations
• Cognitive Neuroscience — 30 citations into it; #3,545 of 10,852 by trust here, #4,925 by citations
• Optimization & Training — 26 citations into it
• Model Architectures — 19 citations into it
• Electrical and Electronic Engineering — 14 citations into it; #4,267 of 16,539 by trust here, #8,038 by citations
• Neuroscience — 13 citations into it

Whom Peter O'Connor trusts overall

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

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

Who trusts Peter O'Connor overall

1. Daniel Neil
2. Shih-Chii Liu
3. Hesham Mostafa
4. Peter U. Diehl
5. Giacomo Indiveri
6. Geoffrey E. Hinton
7. Sumit Bam Shrestha
8. Garrick Orchard
9. S. R. Kheradpisheh
10. Morgane Rivière

Papers from Peter O'Connor's perspective

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

Their papers

• Putting An End to End-to-End: Gradient-Isolated Learning of Representations (2019) — 168 citations
• Real-time classification and sensor fusion with a spiking deep belief network (2013) — 390 citations
• Greedy InfoMax for Biologically Plausible Self-Supervised Representation Learning (2019) — 5 citations