Bas R. Steunebrink
Artificial Intelligence · 2012–2015
A researcher in the Science corpus: 2 papers; trusts 24 researchers and is trusted by 47 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 28 citations into it; #4,781 of 35,345 by trust here, #3,035 by citations
- Model Architectures — 19 citations into it; #892 of 2,278 by trust here, #367 by citations
- Computer Vision and Pattern Recognition — 9 citations into it; #7,237 of 21,575 by trust here, #2,494 by citations
- Natural Language Processing — 9 citations into it
- Optimization & Training — 8 citations into it
- Signal Processing — 8 citations into it; #2,216 of 7,572 by trust here, #272 by citations
- Reinforcement Learning — 5 citations into it; #1,028 of 2,242 by trust here, #1,586 by citations
Whom Bas R. Steunebrink trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- J. Urgen Schmidhuber
- Alex Graves
- Radford M. Neal
- Geoffrey E. Hinton
- Yoshua Bengio
- Ray J. Solomonoff
- Paul J. Werbos
- Richard S. Sutton
- Sepp Hochreiter
- Andrew Barto
Who trusts Bas R. Steunebrink overall
- J. Urgen Schmidhuber
- David R Ha
- Glen Berseth
- Yoshua Bengio
- Sergey Levine
- Shaojie Bai
- Quoc V. Le
- Graham Neubig
- Mohit Bansal
- Kyunghyun Cho
Papers from Bas R. Steunebrink's perspective
Ordered by how strongly the researchers Bas R. Steunebrink 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)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Learning Multiple Layers of Features from Tiny Images
- ImageNet: A large-scale hierarchical image database
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Going deeper with convolutions
Their papers
- LSTM: A Search Space Odyssey (2015) — 6,287 citations
- First Experiments with PowerPlay (2012) — 52 citations