Kyriacos Shiarlis

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 4 papers; trusts 214 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 107 citations into it; #19,353 of 35,345 by trust here, #15,236 by citations
• Reinforcement Learning — 62 citations into it
• Computer Vision and Pattern Recognition — 56 citations into it
• Meta & Continual Learning — 27 citations into it; #640 of 1,041 by trust here, #369 by citations
• Representation & Self-Supervised Learning — 20 citations into it
• Control and Systems Engineering — 19 citations into it
• Optimization & Training — 16 citations into it; #1,786 of 2,601 by trust here, #1,134 by citations
• Probabilistic & Bayesian Methods — 15 citations into it

Whom Kyriacos Shiarlis trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Chelsea Finn
4. Diederik P. Kingma
5. Pieter Abbeel
6. Sergey Levine
7. Yoshua Bengio
8. Alex Krizhevsky
9. Ross B. Girshick
10. Ilya Sutskever

Who trusts Kyriacos Shiarlis overall

1. Annie Xie
2. Chelsea Finn
3. Luckeciano Carvalho Melo
4. Sae-Young Chung
5. Ron Dorfman
6. James Harrison
7. Tianhe Yu
8. Sergey Levine
9. Eric Mitchell
10. Alireza Fallah

Papers from Kyriacos Shiarlis's perspective

Ordered by how strongly the researchers Kyriacos Shiarlis 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. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• CAML: Fast Context Adaptation via Meta-Learning (2018) — 38 citations
• VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning (2019) — 331 citations
• Learning From Demonstration in the Wild (2018) — 77 citations
• Fast Context Adaptation via Meta-Learning (2018) — 432 citations