Can Xu
Artificial Intelligence · with Zhao · 2020–2024
A researcher in the Science corpus: 11 papers; trusts 559 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 497 citations into it
- Natural Language Processing — 222 citations into it
- Computer Vision and Pattern Recognition — 188 citations into it
- Large Language Models — 186 citations into it
- Evaluation & Benchmarks — 72 citations into it
- Multimodal Learning — 54 citations into it
- Efficiency & Compression — 30 citations into it
- Model Architectures — 23 citations into it
Whom Can Xu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Geoffrey E. Hinton
- Yoshua Bengio
- Ilya Sutskever
- Kaiming He
- Alex Krizhevsky
- Yann LeCun
- Jimmy Ba
- Oriol Vinyals
- Alex Graves
Who trusts Can Xu overall
- Jia Li
- Fanxu Meng
- Naman Jain
- Yufei Wang
- Yunfan Shao
- Lianmin Zheng
- Wei-Lin Chiang
- Guan Wang
- Zhiyuan Zeng
- Subhabrata Mukherjee
Papers from Can Xu's perspective
Ordered by how strongly the researchers Can Xu trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- 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
- Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone (2024) — 2,340 citations
- Learning to Ground Visual Objects for Visual Dialog (2021) — 18 citations
- ProphetNet-X: Large-Scale Pre-training Models for English, Chinese, Multi-lingual, Dialog, and Code Generation (2021) — 55 citations
- Low-Resource Knowledge-Grounded Dialogue Generation (2020) — 117 citations
- Are Pre-trained Language Models Knowledgeable to Ground Open Domain Dialogues? (2020) — 9 citations