Timothée Masquelier

Electrical and Electronic Engineering · 2007–2023

A researcher in the Science corpus: 9 papers; trusts 246 researchers and is trusted by 39 researchers here, as inferred from the citation graph.

Subfields

• Electrical and Electronic Engineering — 192 citations into it; #715 of 16,539 by trust here, #3,854 by citations
• Cognitive Neuroscience — 187 citations into it; #847 of 10,852 by trust here, #1,972 by citations
• Artificial Intelligence — 109 citations into it
• Cellular and Molecular Neuroscience — 104 citations into it; #1,842 of 2,998 by trust here, #1,066 by citations
• Computer Vision and Pattern Recognition — 74 citations into it
• Model Architectures — 45 citations into it
• Neuroscience — 36 citations into it
• Optimization & Training — 21 citations into it

Whom Timothée Masquelier trusts overall

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

1. Tomaso A. Poggio
2. Kunihiko Fukushima
3. Alex Krizhevsky
4. Yann LeCun
5. Thomas Serre
6. Yoshua Bengio
7. Geoffrey E. Hinton
8. Guoqiang Bi
9. Maximilian Riesenhuber
10. Simon J. Thorpe

Who trusts Timothée Masquelier overall

1. Sergio Davies
2. Carlos Zamarreño-Ramos
3. S. R. Kheradpisheh
4. Michael Pfeiffer
5. Dean Wyatte
6. Garrick Orchard
7. Amirhossein Tavanaei
8. Bing Han
9. Junhaeng Lee
10. Leyla Isik

Papers from Timothée Masquelier's perspective

Ordered by how strongly the researchers Timothée Masquelier 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. 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. ImageNet: A large-scale hierarchical image database
7. Going deeper with convolutions
8. ImageNet Large Scale Visual Recognition Challenge
9. Attention is All you Need (Neural Information Processing Systems)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Unsupervised Learning of Visual Features through Spike Timing Dependent Plasticity (2007) — 581 citations
• Competitive STDP-Based Spike Pattern Learning (2009) — 330 citations
• STDP and STDP variations with memristors for spiking neuromorphic learning systems (2013) — 489 citations
• Deep Learning in Spiking Neural Networks (2018) — 1,376 citations
• STDP-based spiking deep neural networks for object recognition (2016) — 458 citations
• Deep Residual Learning in Spiking Neural Networks (2021) — 793 citations
• Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks (2020) — 776 citations
• SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence (2023) — 522 citations
• On Spike-Timing-Dependent-Plasticity, Memristive Devices, and Building a Self-Learning Visual Cortex (2011) — 524 citations