Akansel Cosgun

Artificial Intelligence · 2011–2018

A researcher in the Science corpus: 4 papers; trusts 50 researchers and is trusted by 83 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 34 citations into it; #3,127 of 35,345 by trust here, #7,606 by citations
• Reinforcement Learning — 27 citations into it; #327 of 2,242 by trust here, #756 by citations
• Cognitive Neuroscience — 14 citations into it
• Computer Vision and Pattern Recognition — 14 citations into it; #18,281 of 21,575 by trust here, #18,266 by citations
• Representation & Self-Supervised Learning — 10 citations into it
• Model Architectures — 9 citations into it
• Optimization & Training — 9 citations into it
• Cellular and Molecular Neuroscience — 8 citations into it

Whom Akansel Cosgun trusts overall

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

1. J. Urgen Schmidhuber
2. Geoffrey E. Hinton
3. Matthew Thomas Mason
4. Yoshua Bengio
5. Richard S. Sutton
6. Alex Krizhevsky
7. Burr Settles
8. Christopher J. C. H. Watkins
9. Ian J. Goodfellow
10. Yann LeCun

Who trusts Akansel Cosgun overall

1. Rahaf Aljundi
2. Chelsea Finn
3. Christos Kaplanis
4. Arslan Chaudhry
5. Massimo Caccia
6. Jorge Armando Mendez Mendez
7. Sergey Levine
8. Matthias De Lange
9. Christian Geishauser
10. Oleksiy Ostapenko

Papers from Akansel Cosgun's perspective

Ordered by how strongly the researchers Akansel Cosgun 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. GENERATIVE ADVERSARIAL NETS
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

• Push planning for object placement on cluttered table surfaces (2011) — 133 citations
• Selective Experience Replay for Lifelong Learning (2018) — 543 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