Tianjun Xiao

Artificial Intelligence · 2014–2024

A researcher in the Science corpus: 6 papers; trusts 188 researchers and is trusted by 33 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 108 citations into it; #11,526 of 35,345 by trust here, #5,424 by citations
• Computer Vision and Pattern Recognition — 99 citations into it; #4,939 of 21,575 by trust here, #1,404 by citations
• Information Systems — 8 citations into it
• ML Systems & Hardware — 8 citations into it; #296 of 575 by trust here, #51 by citations
• Hardware and Architecture — 5 citations into it

Whom Tianjun Xiao trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Ross B. Girshick
4. Yoshua Bengio
5. Li Fei-Fei
6. Ilya Sutskever
7. Rob Fergus
8. Jia Deng
9. Kaiming He
10. Andrew Zisserman

Who trusts Tianjun Xiao overall

1. Umberto Michieli
2. Xiangteng He
3. Heliang Zheng
4. Yuxin Peng
5. Deboleena Roy
6. Pravendra Singh
7. Jianlong Fu
8. Fei Ye
9. Bo Zhao
10. Pietro Zanuttigh

Papers from Tianjun Xiao's perspective

Ordered by how strongly the researchers Tianjun Xiao 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. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
8. ImageNet Large Scale Visual Recognition Challenge
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems (2015) — 2,339 citations
• The application of two-level attention models in deep convolutional neural network for fine-grained image classification (2014) — 824 citations
• Error-Driven Incremental Learning in Deep Convolutional Neural Network for Large-Scale Image Classification (2014) — 256 citations
• Hallucination of Multimodal Large Language Models: A Survey (2024) — 425 citations
• Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks. (2019) — 1,357 citations
• Minerva: A Scalable and Highly Efficient Training Platform for Deep Learning (2014) — 28 citations