Cornelius Weber

Artificial Intelligence · 2012–2021

A researcher in the Science corpus: 7 papers; trusts 192 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 148 citations into it; #9,869 of 35,345 by trust here, #12,761 by citations
• Cognitive Neuroscience — 94 citations into it; #8,896 of 10,852 by trust here, #9,744 by citations
• Computer Vision and Pattern Recognition — 82 citations into it; #5,667 of 21,575 by trust here, #8,021 by citations
• Natural Language Processing — 53 citations into it
• Neuroscience — 44 citations into it
• Reinforcement Learning — 35 citations into it
• Representation & Self-Supervised Learning — 23 citations into it
• Electrical and Electronic Engineering — 20 citations into it

Whom Cornelius Weber trusts overall

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

1. German Ignacio Parisi
2. Teuvo Kohonen
3. Martin A. Giese
4. Alex Krizhevsky
5. Bernd Fritzke
6. Geoffrey E. Hinton
7. Yoshua Bengio
8. Tomaso A. Poggio
9. Oliver Beyer
10. Yann LeCun

Who trusts Cornelius Weber overall

1. German Ignacio Parisi
2. Luiza Mici
3. Stefan Wermter
4. Jun Tani
5. Tyler L. Hayes
6. Vincenzo Lomonaco
7. Jun Wan
8. Nourhan Elfaramawy
9. Francisco Cruz
10. Nikhil Churamani

Papers from Cornelius Weber's perspective

Ordered by how strongly the researchers Cornelius Weber 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. Gradient-based learning applied to document recognition
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Attention is All you Need (Neural Information Processing Systems)
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

• Lifelong Learning of Spatiotemporal Representations With Dual-Memory Recurrent Self-Organization (2018) — 134 citations
• Self-organizing neural integration of pose-motion features for human action recognition (2015) — 88 citations
• Survey on reinforcement learning for language processing (2021) — 148 citations
• Lifelong learning of human actions with deep neural network self-organization (2017) — 128 citations
• Emergence of multimodal action representations from neural network self-organization (2017) — 43 citations
• Human Action Recognition with Hierarchical Growing Neural Gas Learning (2014) — 22 citations
• A predictive network architecture for a robust and smooth robot docking behavior (2012) — 11 citations