Johannes Schemmel

Electrical and Electronic Engineering · 2006–2019

A researcher in the Science corpus: 5 papers; trusts 9 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Electrical and Electronic Engineering — 35 citations into it; #4,560 of 16,539 by trust here, #4,044 by citations
• Cognitive Neuroscience — 30 citations into it; #5,689 of 10,852 by trust here, #2,465 by citations
• Artificial Intelligence — 21 citations into it; #23,712 of 35,345 by trust here, #15,866 by citations
• Cellular and Molecular Neuroscience — 17 citations into it; #2,056 of 2,998 by trust here, #2,821 by citations
• Statistical and Nonlinear Physics — 5 citations into it

Whom Johannes Schemmel trusts overall

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

1. Peter S. Dayan
2. Romain Brette
3. Sen Song
4. L. F. Abbott
5. Wulfram Gerstner
6. Diederik P. Kingma
7. Geoffrey E. Hinton
8. Chris L. Baker
9. Kenneth D. Miller
10. Sepp Hochreiter

Who trusts Johannes Schemmel overall

1. S. Furber
2. Shimeng Yu
3. Sergio Davies
4. Jennifer Olson Hasler
5. Friedemann Zenke
6. Wolfgang Maass
7. Michael Pfeiffer
8. Bo Marr
9. Catherine D. Schuman
10. Giacomo Indiveri

Papers from Johannes Schemmel's perspective

Ordered by how strongly the researchers Johannes Schemmel trusts endorse each paper. Same corpus, a different order for every researcher.

1. Gradient-based learning applied to document recognition
2. Long Short-Term Memory
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Learning Multiple Layers of Features from Tiny Images
5. ImageNet classification with deep convolutional neural networks
6. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
7. Going deeper with convolutions
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Reading Digits in Natural Images with Unsupervised Feature Learning
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Neuromorphic Silicon Neuron Circuits (2011) — 1,751 citations
• Implementing Synaptic Plasticity in a VLSI Spiking Neural Network Model (2006) — 164 citations
• A wafer-scale neuromorphic hardware system for large-scale neural modeling (2010) — 727 citations
• The Heidelberg Spiking Data Sets for the Systematic Evaluation of Spiking Neural Networks (2019) — 337 citations
• Realizing biological spiking network models in a configurable wafer-scale hardware system (2008) — 78 citations