Fei Wu

Artificial Intelligence · International Joint Conference on Artificial Intelligence · 2013–2023

A researcher in the Science corpus: 26 papers; trusts 1,304 researchers and is trusted by 54 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 558 citations into it; #15,684 of 35,345 by trust here, #3,304 by citations
• Computer Vision and Pattern Recognition — 440 citations into it; #4,294 of 21,575 by trust here, #2,443 by citations
• Natural Language Processing — 236 citations into it; #5,645 of 5,688 by trust here, #2,695 by citations
• Representation & Self-Supervised Learning — 113 citations into it
• Model Architectures — 91 citations into it
• Meta & Continual Learning — 74 citations into it
• Optimization & Training — 62 citations into it
• Evaluation & Benchmarks — 56 citations into it

Whom Fei Wu trusts overall

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

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

Who trusts Fei Wu overall

1. Zhou Zhao
2. Kaiye Wang
3. Yuxin Peng
4. Vincent Wenchen Zheng
5. Senzhang Wang
6. Hongyun Cai
7. Mohammad Rostami
8. Zhou Yu
9. Xin Huang
10. Yueting Zhuang

Papers from Fei Wu's perspective

Ordered by how strongly the researchers Fei Wu 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
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. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Video Question Answering via Gradually Refined Attention over Appearance and Motion (2017) — 817 citations
• Hierarchical Recurrent Neural Encoder for Video Representation with Application to Captioning (2015) — 397 citations
• A Unified MRC Framework for Named Entity Recognition (2019) — 701 citations
• Supervised Coupled Dictionary Learning with Group Structures for Multi-modal Retrieval (2013) — 180 citations
• CorefQA: Coreference Resolution as Query-based Span Prediction (2019) — 196 citations
• Discriminative coupled dictionary hashing for fast cross-media retrieval (2014) — 150 citations
• DeepSaliency: Multi-Task Deep Neural Network Model for Salient Object Detection (2015) — 511 citations
• Description Based Text Classification with Reinforcement Learning (2020) — 71 citations
• Open-Ended Long-form Video Question Answering via Adaptive Hierarchical Reinforced Networks (2018) — 61 citations
• Memory-Efficient Class-Incremental Learning for Image Classification (2020) — 108 citations
• MgSvF: Multi-Grained Slow versus Fast Framework for Few-Shot Class-Incremental Learning (2020) — 151 citations
• Dice Loss for Data-imbalanced NLP Tasks (2019) — 765 citations
• Learning Dynamic Context Augmentation for Global Entity Linking (2019) — 71 citations
• GPT-NER: Named Entity Recognition via Large Language Models (2023) — 420 citations
• Amazon SageMaker Model Parallelism: A General and Flexible Framework for Large Model Training (2021) — 32 citations
• BertGCN: Transductive Text Classification by Combining GNN and BERT (2021) — 317 citations
• FcaNet: Frequency Channel Attention Networks (2020) — 1,135 citations
• Federated Mutual Learning (2020) — 173 citations
• Dynamic Network Embedding by Modeling Triadic Closure Process (2018) — 508 citations
• HST-LSTM: A Hierarchical Spatial-Temporal Long-Short Term Memory Network for Location Prediction (2018) — 314 citations