Jürgen T. Geiger

Signal Processing · 2014–2017

A researcher in the Science corpus: 3 papers; trusts 42 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Signal Processing — 83 citations into it; #6,489 of 7,572 by trust here, #3,913 by citations
• Artificial Intelligence — 74 citations into it; #30,342 of 35,345 by trust here, #21,316 by citations
• Speech & Audio — 46 citations into it
• Computer Vision and Pattern Recognition — 20 citations into it
• Computational Mechanics — 18 citations into it
• Model Architectures — 18 citations into it
• Natural Language Processing — 16 citations into it
• Cognitive Neuroscience — 12 citations into it

Whom Jürgen T. Geiger trusts overall

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

1. Geoffrey E. Hinton
2. Alex Graves
3. Yoshua Bengio
4. Emmanuel Vincent
5. Oriol Vinyals
6. Sepp Hochreiter
7. Yann LeCun
8. J. Urgen Schmidhuber
9. Jon Barker
10. Alex Krizhevsky

Who trusts Jürgen T. Geiger overall

1. Xiangang Li
2. Zhuo Chen
3. Zixing Zhang
4. Xihong Wu
5. Shinji Watanabe
6. Felix Weninger
7. Hakan Erdogan
8. Nantheera Anantrasirichai
9. J. Urgen Schmidhuber

Papers from Jürgen T. Geiger's perspective

Ordered by how strongly the researchers Jürgen T. Geiger trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Robust speech recognition using long short-term memory recurrent neural networks for hybrid acoustic modelling (2014) — 75 citations
• Deep Learning for Environmentally Robust Speech Recognition (2017) — 344 citations
• The MERL/MELCO/TUM system for the REVERB Challenge using Deep Recurrent Neural Network Feature Enhancement (2014) — 44 citations