Friedrich T. Sommer

Cognitive Neuroscience · 1996–2020

A researcher in the Science corpus: 10 papers; trusts 325 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Cognitive Neuroscience — 88 citations into it; #2,049 of 10,852 by trust here, #5,990 by citations
• Artificial Intelligence — 76 citations into it; #10,711 of 35,345 by trust here, #17,776 by citations
• Computer Vision and Pattern Recognition — 70 citations into it; #16,905 of 21,575 by trust here, #19,310 by citations
• Cellular and Molecular Neuroscience — 56 citations into it; #1,924 of 2,998 by trust here, #2,919 by citations
• Generative Models — 42 citations into it; #1,555 of 1,956 by trust here, #1,635 by citations
• Probabilistic & Bayesian Methods — 42 citations into it; #1,284 of 1,525 by trust here, #1,136 by citations
• Electrical and Electronic Engineering — 35 citations into it; #2,174 of 16,539 by trust here, #9,011 by citations
• Neuroscience — 28 citations into it

Whom Friedrich T. Sommer trusts overall

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

1. Andreas Knoblauch
2. Geoffrey E. Hinton
3. Günther Palm
4. Diederik P. Kingma
5. Alex Krizhevsky
6. Radford M. Neal
7. John J. Hopfield
8. Kaiming He
9. Yoshua Bengio
10. Ruslan Salakhutdinov

Who trusts Friedrich T. Sommer overall

1. Andreas Knoblauch
2. Günther Palm
3. Edgar Körner
4. Rein Houthooft
5. Leshem Choshen
6. Ursula Körner
7. Xi Chen
8. Arash Vahdat

Papers from Friedrich T. Sommer's perspective

Ordered by how strongly the researchers Friedrich T. Sommer 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. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Learning and exploration in action-perception loops (2013) — 126 citations
• Learning Energy-Based Models in High-Dimensional Spaces with Multiscale Denoising-Score Matching (2019) — 34 citations
• Memory Capacities for Synaptic and Structural Plasticity (2010) — 144 citations
• Synaptic plasticity, conduction delays, and inter-areal phase relations of spike activity in a model of reciprocally connected areas (2003) — 31 citations
• Resonator Networks, 1: An Efficient Solution for Factoring High-Dimensional, Distributed Representations of Data Structures (2020) — 81 citations
• Annealed Denoising Score Matching: Learning Energy-Based Models in High-Dimensional Spaces (2019) — 13 citations
• Structural Synaptic Plasticity Has High Memory Capacity and Can Explain Graded Amnesia, Catastrophic Forgetting, and the Spacing Effect (2014) — 45 citations
• Improved bidirectional retrieval of sparse patterns stored by Hebbian learning (1999)
• Iterative retrieval of sparsely coded associative memory patterns (1996)
• Associative Data Storage and Retrieval in Neural Networks (1996)