Xinyu Gong
Computer Vision and Pattern Recognition · 2019–2021
A researcher in the Science corpus: 6 papers; trusts 846 researchers and is trusted by 17 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 221 citations into it; #6,745 of 21,575 by trust here, #5,620 by citations
- Artificial Intelligence — 141 citations into it; #10,158 of 35,345 by trust here, #10,566 by citations
- Generative Models — 74 citations into it; #739 of 1,956 by trust here, #1,181 by citations
- Model Architectures — 50 citations into it
- Optimization & Training — 26 citations into it
- Neural Architecture Search & AutoML — 24 citations into it
- Signal Processing — 20 citations into it
- Efficiency & Compression — 19 citations into it
Whom Xinyu Gong trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Kaiming He
- Geoffrey E. Hinton
- Diederik P. Kingma
- Barret Zoph
- Yoshua Bengio
- Karen Simonyan
- Yann LeCun
- Christian Szegedy
- Ilya Sutskever
Who trusts Xinyu Gong overall
- Shiyu Chang
- Zhangyang Wang
- Wuyang Chen
- Yonggan Fu
- Wenhan Yang
- Yunpeng Chen
- Chen Gao
- Kai Han
- Yi-Wei Chen
- Xin He
Papers from Xinyu Gong's perspective
Ordered by how strongly the researchers Xinyu Gong 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)
- Attention is All you Need (Neural Information Processing Systems)
- 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)
- ImageNet Large Scale Visual Recognition Challenge
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- AutoGAN: Neural Architecture Search for Generative Adversarial Networks (2019) — 289 citations
- EnlightenGAN: Deep Light Enhancement Without Paired Supervision (2019) — 2,438 citations
- FasterSeg: Searching for Faster Real-time Semantic Segmentation (2019) — 209 citations
- Conditional Adversarial Generative Flow for Controllable Image Synthesis (2019) — 48 citations
- Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired Perspective (2021) — 298 citations
- AutoSpeech: Neural Architecture Search for Speaker Recognition (2020) — 61 citations