Kai Arulkumaran

Artificial Intelligence · 2016–2022

A researcher in the Science corpus: 7 papers; trusts 436 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 303 citations into it; #10,161 of 35,345 by trust here, #4,370 by citations
• Reinforcement Learning — 197 citations into it; #999 of 2,242 by trust here, #588 by citations
• Computer Vision and Pattern Recognition — 159 citations into it; #11,059 of 21,575 by trust here, #3,222 by citations
• Generative Models — 67 citations into it; #1,858 of 1,956 by trust here, #832 by citations
• Model Architectures — 59 citations into it; #2,031 of 2,278 by trust here, #1,855 by citations
• Cognitive Neuroscience — 49 citations into it
• Representation & Self-Supervised Learning — 33 citations into it
• Computational Theory and Mathematics — 25 citations into it

Whom Kai Arulkumaran trusts overall

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

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

Who trusts Kai Arulkumaran overall

1. Shree Krishna Sharma
2. William Grant Hatcher
3. Alessio Zappone
4. Yonggan Fu
5. Farzad Niroui
6. Qinglin Yang
7. Julio A. Placed
8. Eric Crawford
9. Xuhong Li
10. Thanh Thi Nguyen

Papers from Kai Arulkumaran's perspective

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

Their papers

• Towards Deep Symbolic Reinforcement Learning (2016) — 236 citations
• Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders (2016) — 734 citations
• A Brief Survey of Deep Reinforcement Learning (2017) — 3,581 citations
• Generative Adversarial Networks: An Overview (2017) — 3,805 citations
• On the link between conscious function and general intelligence in humans and machines (2022) — 31 citations
• Adaptive Neural Trees (2018) — 181 citations
• Classifying Options for Deep Reinforcement Learning (2016) — 22 citations