Xinxin Wang

Electrical and Electronic Engineering · with Bennamoun · 2021

A researcher in the Science corpus: 1 paper; trusts 3 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Electrical and Electronic Engineering — 47 citations into it
• Cognitive Neuroscience — 44 citations into it
• Computer Vision and Pattern Recognition — 19 citations into it
• Neuroscience — 8 citations into it
• Statistical and Nonlinear Physics — 6 citations into it

Whom Xinxin Wang trusts overall

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

1. Geoffrey E. Hinton
2. Ronald J. Williams
3. Diederik P. Kingma
4. Yoshua Bengio
5. Kaiming He
6. Paul J. Werbos
7. Alex Krizhevsky
8. Ross B. Girshick
9. Timothy P. Lillicrap

Papers from Xinxin Wang's perspective

Ordered by how strongly the researchers Xinxin Wang 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
7. Gradient-based learning applied to document recognition
8. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet: A large-scale hierarchical image database

Their papers

• Training Spiking Neural Networks Using Lessons From Deep Learning (2021) — 895 citations