Joachim Ott

Artificial Intelligence · 2016

A researcher in the Science corpus: 1 paper; trusts 7 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 15 citations into it
• Computer Vision and Pattern Recognition — 9 citations into it
• Model Architectures — 7 citations into it
• Natural Language Processing — 7 citations into it
• Efficiency & Compression — 5 citations into it
• Optimization & Training — 5 citations into it

Whom Joachim Ott trusts overall

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

1. Yoshua Bengio
2. Ilya Sutskever
3. Sepp Hochreiter
4. Matthieu Courbariaux
5. Xavier Glorot
6. Geoffrey E. Hinton
7. Kyunghyun Cho
8. Tomas Mikolov
9. J. Urgen Schmidhuber
10. Alex Graves

Who trusts Joachim Ott overall

1. Gabriele Prato
2. Taesik Na
3. By Lei Deng
4. Shuangchen Li
5. Qinyao He
6. Yunhui Guo
7. Ella Charlaix

Papers from Joachim Ott's perspective

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

Their papers

• Recurrent Neural Networks With Limited Numerical Precision (2016) — 80 citations