Hesham Mostafa

Artificial Intelligence · 2015–2019

A researcher in the Science corpus: 6 papers; trusts 591 researchers and is trusted by 44 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 114 citations into it; #3,782 of 35,345 by trust here, #9,968 by citations
• Computer Vision and Pattern Recognition — 76 citations into it; #7,139 of 21,575 by trust here, #12,172 by citations
• Electrical and Electronic Engineering — 73 citations into it; #533 of 16,539 by trust here, #5,871 by citations
• Cognitive Neuroscience — 58 citations into it; #720 of 10,852 by trust here, #3,947 by citations
• Optimization & Training — 41 citations into it; #1,906 of 2,601 by trust here, #2,112 by citations
• Model Architectures — 38 citations into it
• Efficiency & Compression — 33 citations into it; #381 of 1,264 by trust here, #913 by citations
• Cellular and Molecular Neuroscience — 26 citations into it

Whom Hesham Mostafa trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Alex Krizhevsky
4. Yann LeCun
5. Kaiming He
6. Diederik P. Kingma
7. Ilya Sutskever
8. Jimmy Ba
9. James Martens
10. L. Eon Bottou

Who trusts Hesham Mostafa overall

1. Friedemann Zenke
2. Emre O. Neftci
3. Nitin Rathi
4. Wei Fang
5. Michael Pfeiffer
6. Kaushik Roy
7. Sidak Pal Singh
8. Tim Dettmers
9. Jacques Kaiser
10. Amirhossein Tavanaei

Papers from Hesham Mostafa's perspective

Ordered by how strongly the researchers Hesham Mostafa 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Parameter Efficient Training of Deep Convolutional Neural Networks by Dynamic Sparse Reparameterization (2019) — 357 citations
• Surrogate Gradient Learning in Spiking Neural Networks: Bringing the Power of Gradient-based optimization to spiking neural networks (2019) — 1,813 citations
• Supervised Learning Based on Temporal Coding in Spiking Neural Networks (2016) — 398 citations
• Deep Supervised Learning Using Local Errors (2017) — 129 citations
• Synaptic Plasticity Dynamics for Deep Continuous Local Learning (DECOLLE) (2018) — 324 citations
• A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128K synapses (2015) — 662 citations