Lanshun Nie

Computer Vision and Pattern Recognition · 2016–2019

A researcher in the Science corpus: 3 papers; trusts 26 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 47 citations into it
• Artificial Intelligence — 38 citations into it
• Efficiency & Compression — 35 citations into it
• Model Architectures — 14 citations into it
• Optimization & Training — 7 citations into it

Whom Lanshun Nie trusts overall

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

1. Song Han
2. Karen Simonyan

Papers from Lanshun Nie's perspective

Ordered by how strongly the researchers Lanshun Nie trusts endorse each paper. Same corpus, a different order for every researcher.

1. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
2. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
3. ImageNet Large Scale Visual Recognition Challenge
4. ImageNet classification with deep convolutional neural networks
5. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Long Short-Term Memory
9. Going deeper with convolutions
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• SeerNet: Predicting Convolutional Neural Network Feature-Map Sparsity Through Low-Bit Quantization (2019) — 80 citations
• Balanced Sparsity for Efficient DNN Inference on GPU (2018) — 99 citations
• Real-Time Wireless Sensor-Actuator Networks for Industrial Cyber-Physical Systems (2016) — 356 citations