Christos Kaplanis

Artificial Intelligence · 2017–2021

A researcher in the Science corpus: 5 papers; trusts 202 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 100 citations into it; #15,578 of 35,345 by trust here, #11,750 by citations
• Reinforcement Learning — 79 citations into it; #1,473 of 2,242 by trust here, #1,241 by citations
• Computer Vision and Pattern Recognition — 41 citations into it; #13,479 of 21,575 by trust here, #11,083 by citations
• Meta & Continual Learning — 32 citations into it; #556 of 1,041 by trust here, #486 by citations
• Representation & Self-Supervised Learning — 24 citations into it
• Optimization & Training — 18 citations into it
• Model Architectures — 17 citations into it
• Cognitive Neuroscience — 15 citations into it

Whom Christos Kaplanis trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Richard S. Sutton
4. Yoshua Bengio
5. Alex Krizhevsky
6. Yann LeCun
7. Kaiming He
8. Ilya Sutskever
9. J. Urgen Schmidhuber
10. Timothy P. Lillicrap

Who trusts Christos Kaplanis overall

1. Mehrdad Farajtabar
2. Seyed Iman Mirzadeh
3. Maciej Wolczyk
4. Pascal Leimer
5. Razvan Pascanu
6. Vincenzo Lomonaco
7. Liyuan Wang
8. Khimya Khetarpal
9. Fabrice Normandin
10. Hadi Nekoei

Papers from Christos Kaplanis's perspective

Ordered by how strongly the researchers Christos Kaplanis trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Continual Reinforcement Learning with Complex Synapses (2018) — 102 citations
• Feature Control as Intrinsic Motivation for Hierarchical Reinforcement Learning (2017) — 103 citations
• Policy Consolidation for Continual Reinforcement Learning (2019) — 61 citations
• Encoders and Ensembles for Task-Free Continual Learning (2021) — 26 citations
• Continual Reinforcement Learning with Multi-Timescale Replay (2020) — 18 citations