Hua Xu
Artificial Intelligence · IEEE Transactions on Evolutionary Computation · 2015–2019
A researcher in the Science corpus: 4 papers; trusts 54 researchers and is trusted by 13 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 79 citations into it; #10,914 of 35,345 by trust here, #12,974 by citations
- Computational Theory and Mathematics — 61 citations into it; #3,346 of 12,182 by trust here, #3,448 by citations
- Management Science and Operations Research — 14 citations into it; #3,072 of 11,946 by trust here, #3,457 by citations
- Computer Vision and Pattern Recognition — 10 citations into it
- Natural Language Processing — 9 citations into it
- Statistics and Probability — 7 citations into it
- Representation & Self-Supervised Learning — 5 citations into it
Whom Hua Xu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kalyanmoy Deb
- Eckart Zitzler
- Qingfu Zhang
- Hui Li
- Miqing Li
- Shengxiang Yang
- Johannes Bader
- Geoffrey E. Hinton
- Juan José Durillo
- L. V. D. van der Maaten
Who trusts Hua Xu overall
- Ye Tian
- Yanan Sun
- Shouyong Jiang
- Hisao Ishibuchi
- Ran Cheng
- Xing-yi Zhang
- Gary G. Yen
- Ryoji Tanabe
- Wen Li
- Shengxiang Yang
Papers from Hua Xu's perspective
Ordered by how strongly the researchers Hua Xu 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)
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Learning Multiple Layers of Features from Tiny Images
- ImageNet: A large-scale hierarchical image database
- Going deeper with convolutions
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- A New Dominance Relation-Based Evolutionary Algorithm for Many-Objective Optimization (2016) — 666 citations
- Balancing Convergence and Diversity in Decomposition-Based Many-Objective Optimizers (2016) — 380 citations
- Deep Unknown Intent Detection with Margin Loss (2019) — 164 citations
- Chinese comments sentiment classification based on word2vec and SVMperf (2015) — 394 citations