David Isele

Artificial Intelligence · 2016–2018

A researcher in the Science corpus: 6 papers; trusts 180 researchers and is trusted by 80 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 94 citations into it; #3,208 of 35,345 by trust here, #7,413 by citations
• Reinforcement Learning — 77 citations into it; #328 of 2,242 by trust here, #756 by citations
• Representation & Self-Supervised Learning — 41 citations into it
• Computer Vision and Pattern Recognition — 35 citations into it
• Cognitive Neuroscience — 15 citations into it
• Computational Mechanics — 13 citations into it
• Computational Theory and Mathematics — 12 citations into it
• Management Science and Operations Research — 12 citations into it; #521 of 11,946 by trust here, #1,077 by citations

Whom David Isele trusts overall

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

1. Richard S. Sutton
2. Geoffrey E. Hinton
3. J. Urgen Schmidhuber
4. Matthew E. Taylor
5. Jonathan Baxter
6. Ronald J. Williams
7. Yoshua Bengio
8. Alex Krizhevsky
9. Yann LeCun
10. Diederik P. Kingma

Who trusts David Isele overall

1. Mohammad Rostami
2. Arslan Chaudhry
3. Rahaf Aljundi
4. Jorge Armando Mendez Mendez
5. Christos Kaplanis
6. Massimo Caccia
7. Eric Eaton
8. Matthias De Lange
9. Oleksiy Ostapenko
10. Christian Geishauser

Papers from David Isele's perspective

Ordered by how strongly the researchers David Isele 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. GENERATIVE ADVERSARIAL NETS

Their papers

• Selective Experience Replay for Lifelong Learning (2018) — 543 citations
• Using Task Descriptions in Lifelong Machine Learning for Improved Performance and Zero-Shot Transfer (2017) — 42 citations
• CM3: Cooperative Multi-goal Multi-stage Multi-agent Reinforcement Learning (2018) — 87 citations
• Navigating Occluded Intersections with Autonomous Vehicles Using Deep Reinforcement Learning (2018) — 277 citations
• Transferring Autonomous Driving Knowledge on Simulated and Real Intersections (2017) — 23 citations
• Using Task Features for Zero-Shot Knowledge Transfer in Lifelong Learning (2016) — 93 citations