Ting-Wu Chin
Computer Vision and Pattern Recognition · 2018–2019
A researcher in the Science corpus: 3 papers; trusts 174 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 58 citations into it
- Artificial Intelligence — 49 citations into it
- Efficiency & Compression — 43 citations into it
- Model Architectures — 13 citations into it
- Optimization & Training — 6 citations into it
Whom Ting-Wu Chin trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Kaiming He
- Geoffrey E. Hinton
- Diederik P. Kingma
- Yihui He
- Yoshua Bengio
- Yann LeCun
- Haichuan Yang
- Ross B. Girshick
- Ilya Sutskever
Who trusts Ting-Wu Chin overall
Papers from Ting-Wu Chin's perspective
Ordered by how strongly the researchers Ting-Wu Chin trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Long Short-Term Memory
- ImageNet Large Scale Visual Recognition Challenge
- Attention is All you Need (Neural Information Processing Systems)
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Layer-compensated Pruning for Resource-constrained Convolutional Neural Networks (2018) — 49 citations
- Designing Adaptive Neural Networks for Energy-Constrained Image Classification (2018) — 67 citations
- FLightNNs: Lightweight Quantized Deep Neural Networks for Fast and Accurate Inference (2019) — 26 citations