Tien-Ju Yang
Computer Vision and Pattern Recognition · 2016–2021
A researcher in the Science corpus: 8 papers; trusts 979 researchers and is trusted by 76 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 321 citations into it; #3,496 of 21,575 by trust here, #1,918 by citations
- Artificial Intelligence — 183 citations into it; #11,643 of 35,345 by trust here, #5,788 by citations
- Efficiency & Compression — 115 citations into it; #171 of 1,264 by trust here, #243 by citations
- Model Architectures — 62 citations into it
- Electrical and Electronic Engineering — 47 citations into it; #1,189 of 16,539 by trust here, #2,583 by citations
- Media Technology — 26 citations into it
- Optimization & Training — 25 citations into it
- Evaluation & Benchmarks — 19 citations into it
Whom Tien-Ju Yang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Alex Krizhevsky
- Geoffrey E. Hinton
- Yann LeCun
- Karen Simonyan
- Yoshua Bengio
- Ross B. Girshick
- Ilya Sutskever
- Christian Szegedy
- Andrew Zisserman
Who trusts Tien-Ju Yang overall
- Haichuan Yang
- Liang-Chieh Chen
- Md Maruf Hossain Shuvo
- Huiyu Wang
- Diana Wofk
- Mengwei Xu
- Vivienne Sze
- Yoshitomo Matsubara
- Bowen Cheng
- Chaoyang He
Papers from Tien-Ju Yang's perspective
Ordered by how strongly the researchers Tien-Ju Yang 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)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications (2018) — 575 citations
- DeeperLab: Single-Shot Image Parser (2019) — 197 citations
- MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep Networks (2017) — 362 citations
- Designing Energy-Efficient Convolutional Neural Networks Using Energy-Aware Pruning (2016) — 797 citations
- Efficient Processing of Deep Neural Networks: A Tutorial and Survey (2017) — 3,699 citations
- FastDepth: Fast Monocular Depth Estimation on Embedded Systems (2019) — 373 citations
- Eyeriss v2: A Flexible Accelerator for Emerging Deep Neural Networks on Mobile Devices (2018) — 1,023 citations
- NetAdaptV2: Efficient Neural Architecture Search with Fast Super-Network Training and Architecture Optimization (2021) — 33 citations