Simon Wiesler

Artificial Intelligence · 2011–2014

A researcher in the Science corpus: 4 papers; trusts 35 researchers and is trusted by 19 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 28 citations into it; #9,302 of 35,345 by trust here, #16,166 by citations
• Optimization & Training — 18 citations into it; #1,657 of 2,601 by trust here, #1,866 by citations
• Signal Processing — 18 citations into it; #4,103 of 7,572 by trust here, #5,340 by citations
• Natural Language Processing — 10 citations into it; #2,508 of 5,688 by trust here, #4,032 by citations
• Speech & Audio — 7 citations into it; #622 of 1,076 by trust here, #923 by citations
• Computer Vision and Pattern Recognition — 5 citations into it; #12,244 of 21,575 by trust here, #14,478 by citations
• Model Architectures — 5 citations into it

Whom Simon Wiesler trusts overall

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

1. James Martens
2. L. Eon Bottou
3. Geoffrey E. Hinton
4. Frank Torsten Bernd Seide
5. Yann LeCun
6. Brian Kingsbury
7. N. Schraudolph
8. Martin A. Riedmiller
9. Robert Malouf
10. Hervé Bourlard

Who trusts Simon Wiesler overall

1. Albert Zeyer
2. Hermann Ney
3. Ralf Schlüter
4. Lei Huang
5. Sergey Ioffe
6. Zhijian Ou
7. Li Deng
8. Dong Yu
9. Alexander Richard
10. James Martens

Papers from Simon Wiesler's perspective

Ordered by how strongly the researchers Simon Wiesler 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. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Mean-normalized stochastic gradient for large-scale deep learning (2014) — 74 citations
• Investigations on hessian-free optimization for cross-entropy training of deep neural networks (2013) — 33 citations
• RASR/NN: The RWTH neural network toolkit for speech recognition (2014) — 66 citations
• A Convergence Analysis of Log-Linear Training (2011) — 78 citations