Robert A. Legenstein

Artificial Intelligence · 2007–2022

A researcher in the Science corpus: 13 papers; trusts 490 researchers and is trusted by 29 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 99 citations into it; #8,910 of 35,345 by trust here, #9,968 by citations
• Computer Vision and Pattern Recognition — 93 citations into it; #11,311 of 21,575 by trust here, #10,905 by citations
• Cognitive Neuroscience — 86 citations into it; #1,648 of 10,852 by trust here, #1,882 by citations
• Electrical and Electronic Engineering — 82 citations into it; #3,380 of 16,539 by trust here, #4,432 by citations
• Cellular and Molecular Neuroscience — 46 citations into it
• Model Architectures — 28 citations into it
• Efficiency & Compression — 23 citations into it; #842 of 1,264 by trust here, #899 by citations
• Generative Models — 22 citations into it

Whom Robert A. Legenstein trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Yoshua Bengio
4. Kaiming He
5. Alex Krizhevsky
6. Yann LeCun
7. Max Welling
8. Ronald J. Williams
9. Radford M. Neal
10. Richard S. Sutton

Who trusts Robert A. Legenstein overall

1. J. Urgen Schmidhuber
2. Nitish Srivastava
3. Wendelin Böhmer
4. Rico Jonschkowski
5. Christian A. Rössert
6. Jelle Munk
7. Miguel Angel Muñoz
8. Oliver Brock
9. Jost Tobias Springenberg
10. Jan Koutník

Papers from Robert A. Legenstein's perspective

Ordered by how strongly the researchers Robert A. Legenstein 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Reinforcement Learning on Slow Features of High-Dimensional Input Streams (2010) — 95 citations
• Long short-term memory and Learning-to-learn in networks of spiking neurons (2018) — 601 citations
• Deep Rewiring: Training very sparse deep networks (2017) — 311 citations
• Restoring Vision in Adverse Weather Conditions With Patch-Based Denoising Diffusion Models (2022) — 509 citations
• A reward-modulated Hebbian learning rule can explain experimentally observed network reorganization in a brain control task (2010) — 144 citations
• Integration of nanoscale memristor synapses in neuromorphic computing architectures (2013) — 592 citations
• A solution to the learning dilemma for recurrent networks of spiking neurons (2019) — 631 citations
• 2007 Special Issue: Edge of chaos and prediction of computational performance for neural circuit models (2007) — 474 citations
• Memory-Efficient Deep Learning on a SpiNNaker 2 Prototype (2018) — 74 citations
• Assembly pointers for variable binding in networks of spiking neurons (2016) — 14 citations
• Synaptic Plasticity as Bayesian Inference (2015) — 45 citations
• IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2015) — 46 citations
• 1 Supplementary Materials for : Emergence of complex computational structures from chaotic neural networks through reward-modulated Hebbian learning (2012)