Xinyang Yi
Artificial Intelligence · 2018–2020
A researcher in the Science corpus: 6 papers; trusts 39 researchers and is trusted by 3 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 109 citations into it
- Computer Vision and Pattern Recognition — 68 citations into it; #18,055 of 21,575 by trust here, #7,340 by citations
- Information Systems — 52 citations into it; #5,800 of 8,771 by trust here, #1,331 by citations
- Representation & Self-Supervised Learning — 30 citations into it
- Recommender Systems — 21 citations into it
- Large Language Models — 12 citations into it
- Model Architectures — 11 citations into it
Whom Xinyang Yi trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Yoshua Bengio
- Steffen Rendle
- Diederik P. Kingma
- Kaiming He
- Alex Krizhevsky
- Ilya Sutskever
- Yifan Hu
- R. Caruana
- Yann LeCun
Who trusts Xinyang Yi overall
- Junliang Yu
- Zhe Zhao
- Tiansheng Yao
- Yi Tay
- Wang-Cheng Kang
- Michael Lui
- Derek Zhiyuan Cheng
- Barret Zoph
Papers from Xinyang Yi's perspective
Ordered by how strongly the researchers Xinyang Yi 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)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Learning Multiple Layers of Features from Tiny Images
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
Their papers
- Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts (2018) — 1,663 citations
- Recommending what video to watch next: a multitask ranking system (2019) — 418 citations
- Sampling-bias-corrected neural modeling for large corpus item recommendations (2019) — 344 citations
- Self-supervised Learning for Large-scale Item Recommendations (2020) — 329 citations
- Learning to Embed Categorical Features without Embedding Tables for Recommendation (2020) — 88 citations
- Factorized Deep Retrieval and Distributed TensorFlow Serving (2018)