Rose Yu

Artificial Intelligence · 2016–2023

A researcher in the Science corpus: 9 papers; trusts 142 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 103 citations into it; #21,081 of 35,345 by trust here, #17,776 by citations
• Computer Vision and Pattern Recognition — 48 citations into it
• Model Architectures — 27 citations into it
• Natural Language Processing — 27 citations into it
• Signal Processing — 27 citations into it
• Statistical and Nonlinear Physics — 18 citations into it
• Representation & Self-Supervised Learning — 14 citations into it
• Management Science and Operations Research — 13 citations into it

Whom Rose Yu trusts overall

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

1. Ilya Sutskever
2. Oriol Vinyals
3. Yoshua Bengio
4. Geoffrey E. Hinton
5. Alex Krizhevsky
6. Diederik P. Kingma
7. J. Urgen Schmidhuber
8. Sepp Hochreiter
9. Yann LeCun
10. Quoc V. Le

Who trusts Rose Yu overall

1. Zhiyong Cui
2. Haoyi Zhou
3. Senzhang Wang
4. Haixu Wu
5. Huaxiu Yao
6. Xiaoyong Jin
7. SHIYANG LI
8. Ali Shojaie
9. Ruimin Ke
10. David Alexander Tedjopurnomo

Papers from Rose Yu's perspective

Ordered by how strongly the researchers Rose Yu 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Going deeper with convolutions

Their papers

• Socratic Learning: Augmenting Generative Models to Incorporate Latent Subsets in Training Data (2016) — 26 citations
• Long-term Forecasting using Tensor-Train RNNs (2017) — 146 citations
• Long-term Forecasting with TiDE: Time-series Dense Encoder (2023) — 600 citations
• Towards Physics-informed Deep Learning for Turbulent Flow Prediction (2019) — 451 citations
• Neural Lander: Stable Drone Landing Control Using Learned Dynamics (2018) — 350 citations
• Long-term Forecasting using Higher Order Tensor RNNs (2017) — 95 citations
• Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting (2017) — 4,347 citations
• Deep Learning: A Generic Approach for Extreme Condition Traffic Forecasting (2017) — 395 citations
• Socratic Learning (2016)