Yuchen Guo
Computer Vision and Pattern Recognition · with Ding · 2013–2019
A researcher in the Science corpus: 4 papers; trusts 428 researchers and is trusted by 38 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 69 citations into it; #4,457 of 21,575 by trust here, #6,671 by citations
- Artificial Intelligence — 37 citations into it; #19,641 of 35,345 by trust here, #14,438 by citations
- Representation & Self-Supervised Learning — 18 citations into it
- Efficiency & Compression — 12 citations into it
- Computational Mechanics — 11 citations into it; #8,239 of 10,001 by trust here, #5,350 by citations
- Model Architectures — 11 citations into it; #1,613 of 2,278 by trust here, #1,693 by citations
- Media Technology — 8 citations into it
- Information Retrieval & Search — 7 citations into it
Whom Yuchen Guo trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- David G. Lowe
- Kaiming He
- Geoffrey E. Hinton
- Alex Krizhevsky
- Ruslan Salakhutdinov
- Christian Szegedy
- Yoshua Bengio
- Yann LeCun
- Ilya Sutskever
- Diederik P. Kingma
Who trusts Yuchen Guo overall
- Cheng Deng
- Yuxin Peng
- Li Liu
- Zijia Lin
- Xiaohan Ding
- Erkun Yang
- Kaiye Wang
- Pavan Kumar Anasosalu Vasu
- Yuming Shen
- Fumin Shen
Papers from Yuchen Guo's perspective
Ordered by how strongly the researchers Yuchen Guo 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
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- 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
- Learning Multiple Layers of Features from Tiny Images
Their papers
- ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks (2019) — 870 citations
- Transfer Sparse Coding for Robust Image Representation (2013) — 197 citations
- Collective Matrix Factorization Hashing for Multimodal Data (2014) — 614 citations
- Latent semantic sparse hashing for cross-modal similarity search (2014) — 478 citations