Felix Alexander Gers

Model Architectures · 1997–2019

A researcher in the Science corpus: 9 papers; trusts 56 researchers and is trusted by 699 researchers here, as inferred from the citation graph.

Subfields

• Model Architectures — 40 citations into it; #95 of 2,278 by trust here, #167 by citations
• Artificial Intelligence — 30 citations into it; #624 of 35,345 by trust here, #1,712 by citations
• Natural Language Processing — 25 citations into it; #1,272 of 5,688 by trust here, #2,106 by citations
• Optimization & Training — 23 citations into it
• Representation & Self-Supervised Learning — 12 citations into it; #322 of 3,145 by trust here, #357 by citations
• Computer Vision and Pattern Recognition — 7 citations into it; #13,811 of 21,575 by trust here, #16,787 by citations
• Safety, Robustness & Interpretability — 6 citations into it
• Large Language Models — 5 citations into it

Whom Felix Alexander Gers trusts overall

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

1. Ronald J. Williams
2. J. Urgen Schmidhuber
3. Sepp Hochreiter
4. P. Rodríguez
5. Barak A. Pearlmutter
6. Yoshua Bengio
7. Paul J. Werbos
8. David Zipser
9. Geoffrey E. Hinton
10. A. Robinson

Who trusts Felix Alexander Gers overall

1. J. Urgen Schmidhuber
2. Zachary Chase Lipton
3. Alex Graves
4. Yoshua Bengio
5. Daan Wierstra
6. Mohit Bansal
7. Quoc V. Le
8. Sergey Levine
9. Kyunghyun Cho
10. Ruslan Salakhutdinov

Papers from Felix Alexander Gers's perspective

Ordered by how strongly the researchers Felix Alexander Gers 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. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. ImageNet Large Scale Visual Recognition Challenge
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Learning to Forget: Continual Prediction with LSTM (2000) — 7,541 citations
• LSTM recurrent networks learn simple context-free and context-sensitive languages (2001) — 760 citations
• Recurrent nets that time and count (2000) — 666 citations
• Long short-term memory in recurrent neural networks (2001) — 274 citations
• Kalman filters improve LSTM network performance in problems unsolvable by traditional recurrent nets (2003) — 109 citations
• How Does BERT Answer Questions?: A Layer-Wise Analysis of Transformer Representations (2019) — 186 citations
• Learning Precise Timing with LSTM Recurrent Networks (2003) — 1,717 citations
• Applying LSTM to Time Series Predictable through Time-Window Approaches (2000) — 489 citations
• "CBM (CAM-BRAIN MACHINE)" A Hardware Tool which Evolves a Neural Net Module in a Fraction of a Second and Runs a Million Neuron Artificial Brain in Real Time (1997)