Luping P. Shi
Computer Vision and Pattern Recognition · 2008–2020
A researcher in the Science corpus: 12 papers; trusts 548 researchers and is trusted by 19 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 202 citations into it; #20,355 of 21,575 by trust here, #7,657 by citations
- Artificial Intelligence — 186 citations into it; #33,229 of 35,345 by trust here, #12,387 by citations
- Electrical and Electronic Engineering — 121 citations into it; #2,092 of 16,539 by trust here, #2,869 by citations
- Efficiency & Compression — 88 citations into it; #1,180 of 1,264 by trust here, #685 by citations
- Model Architectures — 76 citations into it
- Cognitive Neuroscience — 52 citations into it; #3,548 of 10,852 by trust here, #1,769 by citations
- Optimization & Training — 38 citations into it
- Cellular and Molecular Neuroscience — 34 citations into it; #2,976 of 2,998 by trust here, #1,500 by citations
Whom Luping P. Shi trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Geoffrey E. Hinton
- Kaiming He
- Yoshua Bengio
- Yann LeCun
- Karen Simonyan
- Ilya Sutskever
- Diederik P. Kingma
- Christian Szegedy
- Ross B. Girshick
Who trusts Luping P. Shi overall
Papers from Luping P. Shi's perspective
Ordered by how strongly the researchers Luping P. Shi trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Going deeper with convolutions
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Training and Inference with Integers in Deep Neural Networks (2018) — 419 citations
- $L1$ -Norm Batch Normalization for Efficient Training of Deep Neural Networks (2018) — 128 citations
- Spatio-Temporal Backpropagation for Training High-Performance Spiking Neural Networks (2017) — 1,368 citations
- Towards artificial general intelligence with hybrid Tianjic chip architecture (2019) — 1,085 citations
- CIFAR10-DVS: An Event-Stream Dataset for Object Classification (2017) — 710 citations
- Direct Training for Spiking Neural Networks: Faster, Larger, Better (2018) — 797 citations
- Breaking the Speed Limits of Phase-Change Memory (2012) — 784 citations
- Face classification using electronic synapses (2017) — 828 citations
- Fast phase transitions induced by picosecond electrical pulses on phase change memory cells (2008) — 170 citations
- Model Compression and Hardware Acceleration for Neural Networks: A Comprehensive Survey (2020) — 940 citations
- Laser precision engineering: from microfabrication to nanoprocessing (2010) — 164 citations
- Creation of a needle of longitudinally polarized light in vacuum using binary optics (2008) — 860 citations