Yuan Xie

Computer Vision and Pattern Recognition · 2006–2021

A researcher in the Science corpus: 27 papers; trusts 1,340 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 503 citations into it; #9,296 of 21,575 by trust here, #4,690 by citations
• Artificial Intelligence — 334 citations into it
• Electrical and Electronic Engineering — 156 citations into it; #10,060 of 16,539 by trust here, #3,506 by citations
• Efficiency & Compression — 121 citations into it
• Model Architectures — 96 citations into it
• Hardware and Architecture — 86 citations into it; #1,391 of 1,551 by trust here, #318 by citations
• Media Technology — 66 citations into it
• Computer Networks and Communications — 59 citations into it; #7,075 of 9,886 by trust here, #2,687 by citations

Whom Yuan Xie trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Ross B. Girshick
5. Karen Simonyan
6. Yoshua Bengio
7. Yann LeCun
8. Diederik P. Kingma
9. Ilya Sutskever
10. Andrew Zisserman

Who trusts Yuan Xie overall

1. Deng-Ping Fan
2. Yun Miao
3. Gabriel H. Loh
4. Jianlong Wu
5. Yang Sun
6. Jiangjiang Liu
7. Vladimir Braverman
8. Ming-Ming Cheng
9. Zhisheng Lu
10. Thierry Bouwmans

Papers from Yuan Xie's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• $L1$ -Norm Batch Normalization for Efficient Training of Deep Neural Networks (2018) — 128 citations
• Instance-Level Salient Object Segmentation (2017) — 295 citations
• Enhanced Pix2pix Dehazing Network (2019) — 696 citations
• LatticeNet: Towards Lightweight Image Super-Resolution with Lattice Block (2020) — 348 citations
• Off-Policy Evaluation and Learning from Logged Bandit Feedback: Error Reduction via Surrogate Policy (2018) — 21 citations
• Towards artificial general intelligence with hybrid Tianjic chip architecture (2019) — 1,085 citations
• Cambricon: An Instruction Set Architecture for Neural Networks (2016) — 315 citations
• A novel architecture of the 3D stacked MRAM L2 cache for CMPs (2009) — 455 citations
• NVSim: A Circuit-Level Performance, Energy, and Area Model for Emerging Nonvolatile Memory (2012) — 1,223 citations
• Rethinking the performance comparison between SNNS and ANNS (2020) — 355 citations
• Pinatubo: A processing-in-memory architecture for bulk bitwise operations in emerging non-volatile memories (2016) — 445 citations
• DLAU: A Scalable Deep Learning Accelerator Unit on FPGA (2016) — 320 citations
• Design and Management of 3D Chip Multiprocessors Using Network-in-Memory (2006) — 426 citations
• DRISA: A DRAM-based Reconfigurable In-Situ Accelerator (2017) — 446 citations
• Tackling the Qubit Mapping Problem for NISQ-Era Quantum Devices (2018) — 773 citations
• Overcoming the challenges of crossbar resistive memory architectures (2015) — 327 citations
• Processor Design in 3D Die-Stacking Technologies (2007) — 302 citations
• A Survey of Accelerator Architectures for Deep Neural Networks (2020) — 348 citations
• Contrastive Learning for Compact Single Image Dehazing (2021) — 860 citations
• Generalized Latent Multi-View Subspace Clustering (2020) — 650 citations