Congying Xia

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 6 papers; trusts 195 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 196 citations into it; #19,794 of 35,345 by trust here, #15,004 by citations
• Natural Language Processing — 107 citations into it
• Computer Vision and Pattern Recognition — 61 citations into it; #12,479 of 21,575 by trust here, #10,500 by citations
• Representation & Self-Supervised Learning — 46 citations into it
• Model Architectures — 40 citations into it
• Graph Learning — 19 citations into it
• Evaluation & Benchmarks — 18 citations into it
• Information Systems — 17 citations into it

Whom Congying Xia trusts overall

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

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

Who trusts Congying Xia overall

1. Wenpeng Yin
2. Ruiying Geng
3. 刘晗
4. Chenliang Li
5. Philip S. Yu
6. Jianguo Zhang
7. Caiming Xiong
8. Congyin Xia
9. Nikhita Vedula

Papers from Congying Xia's perspective

Ordered by how strongly the researchers Congying Xia 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. 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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• CG-BERT: Conditional Text Generation with BERT for Generalized Few-shot Intent Detection (2020) — 48 citations
• A Survey on Text Classification: From Shallow to Deep Learning (2020) — 173 citations
• Semantic Matching and Aggregation Network for Few-shot Intent Detection (2020) — 29 citations
• Graph-Bert: Only Attention is Needed for Learning Graph Representations (2020) — 391 citations
• Composed Variational Natural Language Generation for Few-shot Intents (2020) — 31 citations
• Zero-shot User Intent Detection via Capsule Neural Networks (2018) — 224 citations