Wei Fan

Artificial Intelligence · Medical Physics (Lancaster) · 2003–2021

A researcher in the Science corpus: 17 papers; trusts 268 researchers and is trusted by 42 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 164 citations into it; #5,549 of 35,345 by trust here, #4,298 by citations
• Computer Vision and Pattern Recognition — 77 citations into it; #11,911 of 21,575 by trust here, #8,409 by citations
• Representation & Self-Supervised Learning — 42 citations into it
• Optimization & Training — 31 citations into it
• Information Systems — 28 citations into it
• Natural Language Processing — 28 citations into it
• Computer Networks and Communications — 25 citations into it; #1,736 of 9,886 by trust here, #1,395 by citations
• Evaluation & Benchmarks — 23 citations into it

Whom Wei Fan trusts overall

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

1. Geoffrey E. Hinton
2. Kaiming He
3. Alex Krizhevsky
4. Diederik P. Kingma
5. Ross B. Girshick
6. Yoshua Bengio
7. Yann LeCun
8. Thorsten Joachims
9. Ilya Sutskever
10. Bernhard Schölkopf

Who trusts Wei Fan overall

1. Ludmila I. Kuncheva
2. Mingsheng Long
3. Qiang Yang
4. Ming Shao
5. Jing Gao
6. Alexey Tsymbal
7. Pedro H. O. Pinheiro
8. Tyler L. Hayes
9. Sinno Jialin Pan
10. Jianmin Wang

Papers from Wei Fan's perspective

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

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

Their papers

• Cross Validation Framework to Choose amongst Models and Datasets for Transfer Learning (2010) — 157 citations
• Knowledge transfer via multiple model local structure mapping (2008) — 349 citations
• Efficient multi-task feature learning with calibration (2014) — 58 citations
• Stochastic Gradient Made Stable: A Manifold Propagation Approach for Large-Scale Optimization (2015) — 36 citations
• Transfer across Completely Different Feature Spaces via Spectral Embedding (2013) — 63 citations
• Multi-source domain adaptation and its application to early detection of fatigue (2011) — 57 citations
• Resolving conflicts in heterogeneous data by truth discovery and source reliability estimation (2014) — 507 citations
• Exploring and Distilling Posterior and Prior Knowledge for Radiology Report Generation (2021) — 393 citations
• AnatomyNet: Deep learning for fast and fully automated whole‐volume segmentation of head and neck anatomy (2018) — 492 citations
• Generative Discovery of Relational Medical Entity Pairs (2018)
• Joint Slot Filling and Intent Detection via Capsule Neural Networks (2018) — 253 citations
• Knowledge-aware Attentive Neural Network for Ranking Question Answer Pairs (2018) — 69 citations
• DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification (2018) — 429 citations
• A Survey on Truth Discovery (2015) — 449 citations
• A Confidence-Aware Approach for Truth Discovery on Long-Tail Data (2014) — 346 citations
• ViST: a dynamic index method for querying XML data by tree structures (2003) — 339 citations
• Mining concept-drifting data streams using ensemble classifiers (2003) — 1,530 citations