Jiaxi Tang
Artificial Intelligence · 2018–2020
A researcher in the Science corpus: 4 papers; trusts 236 researchers and is trusted by 51 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 85 citations into it; #4,008 of 35,345 by trust here, #7,797 by citations
- Information Systems — 54 citations into it; #380 of 8,771 by trust here, #1,246 by citations
- Computer Vision and Pattern Recognition — 50 citations into it; #18,633 of 21,575 by trust here, #19,310 by citations
- Recommender Systems — 20 citations into it
- Management Science and Operations Research — 18 citations into it
- Natural Language Processing — 16 citations into it
- Efficiency & Compression — 15 citations into it
- Representation & Self-Supervised Learning — 14 citations into it
Whom Jiaxi Tang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Alex Krizhevsky
- Diederik P. Kingma
- Yoon Kim
- Ruslan Salakhutdinov
- Jimmy Ba
- Yoshua Bengio
- Steffen Rendle
- Kaiming He
- Ilya Sutskever
Who trusts Jiaxi Tang overall
- Yupeng Hou
- Fajie Yuan
- Jiacheng Li
- Kun Zhou
- Xu Chen
- Julian McAuley
- Xiangnan He
- Fei Sun
- Wang-Cheng Kang
- Aminu Da’u
Papers from Jiaxi Tang's perspective
Ordered by how strongly the researchers Jiaxi Tang 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)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Understanding and Improving Knowledge Distillation (2020) — 164 citations
- Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding (2018) — 2,242 citations
- Ranking Distillation: Learning Compact Ranking Models With High Performance for Recommender System (2018) — 231 citations
- Sequential Recommendation with User Memory Networks (2018) — 544 citations