Thomas Natschläger

Artificial Intelligence · 2002–2017

A researcher in the Science corpus: 4 papers; trusts 29 researchers and is trusted by 65 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 21 citations into it; #3,236 of 35,345 by trust here, #3,304 by citations
• Cognitive Neuroscience — 20 citations into it; #406 of 10,852 by trust here, #624 by citations
• Electrical and Electronic Engineering — 14 citations into it; #514 of 16,539 by trust here, #2,466 by citations
• Computer Vision and Pattern Recognition — 13 citations into it
• Representation & Self-Supervised Learning — 11 citations into it
• Model Architectures — 5 citations into it

Whom Thomas Natschläger trusts overall

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

1. John A. Hertz
2. Jordan B. Pollack
3. Wolfgang Maass
4. Alex Krizhevsky
5. Geoffrey E. Hinton
6. Henry Markram
7. Anders Krogh
8. Karen Simonyan
9. Yoshua Bengio
10. Baochen Sun

Who trusts Thomas Natschläger overall

1. Herbert Jaeger
2. John M. Beggs
3. Mantas Lukosevicius
4. Wolfgang Maass
5. Filip Ponulak
6. Greg Yang
7. Rafael Yuste
8. David Sussillo
9. Kohei Nakajima
10. Mark S. Goldman

Papers from Thomas Natschläger's perspective

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

Their papers

• Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations (2002) — 4,178 citations
• Central Moment Discrepancy (CMD) for Domain-Invariant Representation Learning (2017) — 675 citations
• Simulation of networks of spiking neurons: A review of tools and strategies (2006) — 884 citations
• Real-Time Computation at the Edge of Chaos in Recurrent Neural Networks (2004) — 841 citations