Tim Verbelen

Artificial Intelligence · 2012–2019

A researcher in the Science corpus: 5 papers; trusts 97 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 87 citations into it; #19,650 of 35,345 by trust here, #19,439 by citations
• Computer Vision and Pattern Recognition — 63 citations into it; #19,143 of 21,575 by trust here, #12,419 by citations
• Optimization & Training — 33 citations into it
• Computer Networks and Communications — 24 citations into it; #8,604 of 9,886 by trust here, #7,140 by citations
• Representation & Self-Supervised Learning — 10 citations into it; #1,996 of 3,145 by trust here, #2,691 by citations
• Natural Language Processing — 8 citations into it
• Generative Models — 6 citations into it
• Probabilistic & Bayesian Methods — 5 citations into it

Whom Tim Verbelen trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Yoshua Bengio
4. Mahadev Satyanarayanan
5. Yann LeCun
6. Alex Krizhevsky
7. Kaiming He
8. Karen Simonyan
9. L. Eon Bottou
10. Ilya Sutskever

Who trusts Tim Verbelen overall

1. Koustabh Dolui
2. Francesco Locatello
3. Soumya Kanti Datta
4. Tyler L. Hayes

Papers from Tim Verbelen's perspective

Ordered by how strongly the researchers Tim Verbelen 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. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Going deeper with convolutions
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Improving Generalization for Abstract Reasoning Tasks Using Disentangled Feature Representations (2018) — 72 citations
• The cascading neural network: building the Internet of Smart Things (2017) — 58 citations
• A Survey on Distributed Machine Learning (2019) — 881 citations
• Visualizing Convolutional Neural Networks to Improve Decision Support for Skin Lesion Classification (2018) — 59 citations
• Cloudlets: bringing the cloud to the mobile user (2012) — 388 citations