Emre O. Neftci

Electrical and Electronic Engineering · IEEE Signal Processing Magazine · 2018–2021

A researcher in the Science corpus: 3 papers; trusts 88 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Electrical and Electronic Engineering — 80 citations into it; #2,772 of 16,539 by trust here, #7,892 by citations
• Artificial Intelligence — 72 citations into it; #11,601 of 35,345 by trust here, #17,110 by citations
• Cognitive Neuroscience — 72 citations into it; #2,486 of 10,852 by trust here, #4,819 by citations
• Cellular and Molecular Neuroscience — 28 citations into it
• Computer Vision and Pattern Recognition — 27 citations into it
• Optimization & Training — 19 citations into it
• Model Architectures — 17 citations into it
• Neuroscience — 11 citations into it

Whom Emre O. Neftci trusts overall

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

1. Yoshua Bengio
2. Ronald J. Williams
3. Geoffrey E. Hinton
4. Hesham Mostafa
5. Yann LeCun
6. Timothy P. Lillicrap
7. Diederik P. Kingma
8. Alex Krizhevsky
9. Friedemann Zenke
10. Wulfram Gerstner

Who trusts Emre O. Neftci overall

1. Wei Fang
2. Nitin Rathi
3. Bing Han
4. Jacques Kaiser
5. Gopalakrishnan Srinivasan
6. Benjamin Cramer
7. Man Yao
8. Zhaofei Yu
9. Catherine D. Schuman
10. Aboozar Taherkhani

Papers from Emre O. Neftci's perspective

Ordered by how strongly the researchers Emre O. Neftci 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Going deeper with convolutions

Their papers

• Surrogate Gradient Learning in Spiking Neural Networks: Bringing the Power of Gradient-based optimization to spiking neural networks (2019) — 1,813 citations
• Synaptic Plasticity Dynamics for Deep Continuous Local Learning (DECOLLE) (2018) — 324 citations
• Training Spiking Neural Networks Using Lessons From Deep Learning (2021) — 895 citations