Jesper E. van Engelen
Artificial Intelligence · 2019
A researcher in the Science corpus: 1 paper; trusts 167 researchers and is trusted by 9 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 69 citations into it; #14,683 of 35,345 by trust here, #16,771 by citations
- Representation & Self-Supervised Learning — 42 citations into it
- Computer Vision and Pattern Recognition — 41 citations into it
- Graph Learning — 13 citations into it
- Generative Models — 10 citations into it
- Optimization & Training — 9 citations into it
- Natural Language Processing — 7 citations into it
- Information Systems — 6 citations into it
Whom Jesper E. van Engelen trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Geoffrey E. Hinton
- Zhi-Hua Zhou
- Yoshua Bengio
- Ian J. Goodfellow
- Ilya Sutskever
- Diederik P. Kingma
- Yann LeCun
- Radford M. Neal
- Ruslan Salakhutdinov
Who trusts Jesper E. van Engelen overall
- Zhaoyi Xu
- Jian Liang
- Md Maruf Hossain Shuvo
- Michael Desmond
- Yi Xu
- Joseph Homer Saleh
- Ran He
- Xiangli Yang
- Syed Kamrul Islam
- Evelyn Duesterwald
Papers from Jesper E. van Engelen's perspective
Ordered by how strongly the researchers Jesper E. van Engelen trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- A survey on semi-supervised learning (2019) — 2,517 citations