Chongyang Tao

Artificial Intelligence · 2017–2024

A researcher in the Science corpus: 9 papers; trusts 234 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 438 citations into it; #15,086 of 35,345 by trust here, #4,254 by citations
• Natural Language Processing — 185 citations into it; #2,865 of 5,688 by trust here, #2,473 by citations
• Computer Vision and Pattern Recognition — 135 citations into it
• Evaluation & Benchmarks — 56 citations into it; #1,643 of 3,147 by trust here, #1,817 by citations
• Representation & Self-Supervised Learning — 30 citations into it
• Model Architectures — 19 citations into it
• Generative Models — 13 citations into it
• Optimization & Training — 12 citations into it

Whom Chongyang Tao trusts overall

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

1. Diederik P. Kingma
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Ilya Sutskever
5. Alex Krizhevsky
6. Jimmy Ba
7. Yann LeCun
8. Kaiming He
9. Alex Graves
10. Oriol Vinyals

Who trusts Chongyang Tao overall

1. Sarik Ghazarian
2. Ananya B. Sai
3. Minlie Huang
4. Jian Guan
5. Yi-Ting Yeh
6. Weixin Liang
7. Rostislav Nedelchev
8. Akash Kumar Mohankumar
9. Lu Li
10. Junlong Li

Papers from Chongyang Tao's perspective

Ordered by how strongly the researchers Chongyang Tao 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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• RUBER: An Unsupervised Method for Automatic Evaluation of Open-Domain Dialog Systems (2017) — 227 citations
• WizardLM: Empowering Large Pre-Trained Language Models to Follow Complex Instructions (2023) — 1,266 citations
• MMDialog: A Large-scale Multi-turn Dialogue Dataset Towards Multi-modal Open-domain Conversation (2022) — 80 citations
• Knowledge-Grounded Dialogue Generation with Pre-trained Language Models (2020) — 206 citations
• WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct (2023) — 752 citations
• WizardCoder: Empowering Code Large Language Models with Evol-Instruct (2023) — 1,012 citations
• A Survey on Knowledge Distillation of Large Language Models (2024) — 355 citations
• Low-Resource Knowledge-Grounded Dialogue Generation (2020) — 117 citations
• Overcoming Catastrophic Forgetting for Continual Learning via Model Adaptation (2018) — 150 citations