Hu Xu

Artificial Intelligence · with Liu · 2016–2022

A researcher in the Science corpus: 15 papers; trusts 612 researchers and is trusted by 62 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 364 citations into it; #3,522 of 35,345 by trust here, #4,577 by citations
• Natural Language Processing — 163 citations into it; #1,898 of 5,688 by trust here, #2,846 by citations
• Computer Vision and Pattern Recognition — 146 citations into it; #16,603 of 21,575 by trust here, #15,162 by citations
• Meta & Continual Learning — 92 citations into it; #229 of 1,041 by trust here, #284 by citations
• Representation & Self-Supervised Learning — 84 citations into it; #2,172 of 3,145 by trust here, #2,355 by citations
• Model Architectures — 42 citations into it
• Reinforcement Learning — 32 citations into it
• Evaluation & Benchmarks — 23 citations into it

Whom Hu Xu trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Sepp Hochreiter
5. Alex Krizhevsky
6. Ilya Sutskever
7. Tomas Mikolov
8. Yann LeCun
9. J. Urgen Schmidhuber
10. Jimmy Ba

Who trusts Hu Xu overall

1. Zixuan Ke
2. Bing Liu
3. Lei Shu
4. B. Liu
5. Sepideh Esmaeilpour
6. Da-Wei Zhou
7. Pei Ke
8. Philip S. Yu
9. Wenpeng Hu
10. Fan-Keng Sun

Papers from Hu Xu's perspective

Ordered by how strongly the researchers Hu Xu 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. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• DOC: Deep Open Classification of Text Documents (2017) — 345 citations
• Lifelong Learning CRF for Supervised Aspect Extraction (2017) — 105 citations
• Lifelong-RL: Lifelong Relaxation Labeling for Separating Entities and Aspects in Opinion Targets (2016) — 34 citations
• Open-world Learning and Application to Product Classification (2018) — 119 citations
• BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis (2019) — 791 citations
• Continual Training of Language Models for Few-Shot Learning (2022) — 45 citations
• Achieving Forgetting Prevention and Knowledge Transfer in Continual Learning (2021) — 159 citations
• Adapting BERT for Continual Learning of a Sequence of Aspect Sentiment Classification Tasks (2021) — 95 citations
• CLASSIC: Continual and Contrastive Learning of Aspect Sentiment Classification Tasks (2021) — 68 citations
• Modeling Multi-Action Policy for Task-Oriented Dialogues (2019) — 11 citations
• Unseen Class Discovery in Open-world Classification (2018) — 76 citations
• Incorporating the Structure of the Belief State in End-to-End Task-Oriented Dialogue Systems (2018)
• Lifelong Domain Word Embedding via Meta-Learning (2018) — 39 citations
• Double Embeddings and CNN-based Sequence Labeling for Aspect Extraction (2018) — 358 citations
• Dual Attention Network for Product Compatibility and Function Satisfiability Analysis (2017) — 13 citations