T. van Laarhoven
Artificial Intelligence · 2011–2017
A researcher in the Science corpus: 3 papers; trusts 13 researchers and is trusted by 77 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 13 citations into it; #3,484 of 35,345 by trust here, #16,771 by citations
- Optimization & Training — 11 citations into it; #420 of 2,601 by trust here, #1,509 by citations
- Computer Vision and Pattern Recognition — 8 citations into it; #3,391 of 21,575 by trust here, #11,286 by citations
Whom T. van Laarhoven trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- James Martens
- Diederik P. Kingma
- Ilya Sutskever
- Geoffrey E. Hinton
- Jimmy Ba
- Yoshua Bengio
- Yann LeCun
- L. Eon Bottou
- Sergey Ioffe
Who trusts T. van Laarhoven overall
- Kazuki Osawa
- Colin Wei
- Hongyi Zhang
- Mehmet Gönen
- Ping Luo
- Boyi Li
- Fatemehsadat Mireshghallah
- Daniel S. Park
- Henry Gouk
- Yohei Tsuji
Papers from T. van Laarhoven's perspective
Ordered by how strongly the researchers T. van Laarhoven 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)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- 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)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- L2 Regularization versus Batch and Weight Normalization (2017) — 344 citations
- Gaussian interaction profile kernels for predicting drug-target interaction (2011) — 844 citations
- Convolutional neural networks for vibrational spectroscopic data analysis. (2017) — 406 citations