Xinyu Yang

Artificial Intelligence · with Amini · 2021–2022

A researcher in the Science corpus: 2 papers; trusts 410 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 158 citations into it
• Computer Vision and Pattern Recognition — 87 citations into it
• Signal Processing — 83 citations into it
• Speech & Audio — 54 citations into it
• Aerospace Engineering — 34 citations into it
• Generative Models — 33 citations into it
• Natural Language Processing — 32 citations into it
• Model Architectures — 19 citations into it

Whom Xinyu Yang trusts overall

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

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

Papers from Xinyu Yang's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation (2022) — 1,613 citations
• Review of end-to-end speech synthesis technology based on deep learning (2021) — 32 citations