Cunchao Tu
Artificial Intelligence · 2016–2021
A researcher in the Science corpus: 10 papers; trusts 167 researchers and is trusted by 5 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 187 citations into it; #25,204 of 35,345 by trust here, #11,750 by citations
- Natural Language Processing — 124 citations into it
- Computer Vision and Pattern Recognition — 52 citations into it
- Representation & Self-Supervised Learning — 40 citations into it
- Model Architectures — 23 citations into it
- Information Systems — 22 citations into it
- Graph Learning — 20 citations into it
- Political Science and International Relations — 20 citations into it; #2,129 of 4,701 by trust here, #590 by citations
Whom Cunchao Tu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Diederik P. Kingma
- Tomas Mikolov
- Yoshua Bengio
- Yoon Kim
- Ilya Sutskever
- Alex Krizhevsky
- Richard Socher
- Yann LeCun
- Sepp Hochreiter
Who trusts Cunchao Tu overall
- Chaojun Xiao
- Haoxiang Zhong
- Ilias Chalkidis
- Jiezhong Qiu
- Xiao Wang
- Chuan Shi
- Daokun Zhang
- Chengwei Qin
- Hongyun Cai
- Feng Xia
Papers from Cunchao Tu's perspective
Ordered by how strongly the researchers Cunchao Tu trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
Their papers
- Neural Sentiment Classification with User and Product Attention (2016) — 315 citations
- CAIL2018: A Large-Scale Legal Dataset for Judgment Prediction (2018) — 370 citations
- Lawformer: A Pre-trained Language Model for Chinese Legal Long Documents (2021) — 351 citations
- How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence (2020) — 435 citations
- JEC-QA: A Legal-Domain Question Answering Dataset (2019) — 220 citations
- CAIL2019-SCM: A Dataset of Similar Case Matching in Legal Domain (2019) — 82 citations
- Few-Shot Charge Prediction with Discriminative Legal Attributes (2018) — 250 citations
- Legal Judgment Prediction via Topological Learning (2018) — 359 citations
- Fast Network Embedding Enhancement via High Order Proximity Approximation (2017) — 155 citations
- Max-Margin DeepWalk: Discriminative Learning of Network Representation (2016) — 263 citations