Pengtao Xie

Artificial Intelligence · 2013–2023

A researcher in the Science corpus: 13 papers; trusts 713 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 243 citations into it; #16,791 of 35,345 by trust here, #5,281 by citations
• Computer Vision and Pattern Recognition — 186 citations into it; #10,651 of 21,575 by trust here, #4,781 by citations
• Model Architectures — 56 citations into it
• Optimization & Training — 54 citations into it
• Natural Language Processing — 47 citations into it; #3,176 of 5,688 by trust here, #2,732 by citations
• Representation & Self-Supervised Learning — 40 citations into it
• Evaluation & Benchmarks — 32 citations into it
• Multimodal Learning — 19 citations into it

Whom Pengtao Xie trusts overall

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

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

Who trusts Pengtao Xie overall

1. Reza Shokri
2. Yuan Li
3. Briland Hitaj
4. Edward Chou
5. O. Ohrimenko
6. Baoyu Jing
7. Eric P. Xing
8. Xiaodan Liang
9. Aurelia Bustos
10. Jun Chen

Papers from Pengtao Xie's perspective

Ordered by how strongly the researchers Pengtao Xie trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. Going deeper with convolutions
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• On the Automatic Generation of Medical Imaging Reports (2017) — 673 citations
• CERT: Contrastive Self-supervised Learning for Language Understanding (2020) — 379 citations
• PathVQA: 30000+ Questions for Medical Visual Question Answering (2020) — 536 citations
• Not All Patches are What You Need: Expediting Vision Transformers via Token Reorganizations (2022) — 457 citations
• Petuum: A New Platform for Distributed Machine Learning on Big Data (2013) — 526 citations
• Sample-Efficient Deep Learning for COVID-19 Diagnosis Based on CT Scans (2020) — 393 citations
• Crypto-Nets: Neural Networks over Encrypted Data (2014) — 160 citations
• Poseidon: An Efficient Communication Architecture for Distributed Deep Learning on GPU Clusters (2017) — 378 citations
• A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT (2023) — 736 citations
• COVID-CT-Dataset: A CT Scan Dataset about COVID-19 (2020) — 910 citations
• Differentially-private Federated Neural Architecture Search (2020) — 25 citations
• Specific and Shared Causal Relation Modeling and Mechanism-Based Clustering (2019) — 28 citations
• Strategies and Principles of Distributed Machine Learning on Big Data (2015) — 167 citations