Hsiang-Fu Yu

Artificial Intelligence · 2009–2020

A researcher in the Science corpus: 10 papers; trusts 92 researchers and is trusted by 60 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 92 citations into it; #5,983 of 35,345 by trust here, #7,241 by citations
• Computer Vision and Pattern Recognition — 41 citations into it; #19,309 of 21,575 by trust here, #18,266 by citations
• Optimization & Training — 36 citations into it
• Computational Mechanics — 29 citations into it; #4,295 of 10,001 by trust here, #4,600 by citations
• Signal Processing — 19 citations into it; #5,861 of 7,572 by trust here, #3,784 by citations
• Natural Language Processing — 18 citations into it
• Management Science and Operations Research — 17 citations into it; #9,117 of 11,946 by trust here, #5,092 by citations
• Information Systems — 14 citations into it; #4,288 of 8,771 by trust here, #4,012 by citations

Whom Hsiang-Fu Yu trusts overall

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

1. Chih-Jen Lin
2. Alex Smola
3. Geoffrey E. Hinton
4. L. Eon Bottou
5. Sham M. Kakade
6. Inderjit S. Dhillon
7. B. Recht
8. Prateek Jain
9. Cho-Jui Hsieh
10. Shai Shalev-Shwartz

Who trusts Hsiang-Fu Yu overall

1. Cho-Jui Hsieh
2. Inderjit S. Dhillon
3. Yong Zhuang
4. Jinsung Yoon
5. Panagiotis Adamopoulos
6. Wei-Sheng Chin
7. Bernie Wang
8. Jan Gasthaus
9. Syama Sundar Rangapuram
10. Xiaoyong Jin

Papers from Hsiang-Fu Yu's perspective

Ordered by how strongly the researchers Hsiang-Fu 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. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• NOMAD: Nonlocking, stOchastic Multi-machine algorithm for Asynchronous and Decentralized matrix completion (2013) — 123 citations
• Large-scale Multi-label Learning with Missing Labels (2013) — 515 citations
• Dual coordinate descent methods for logistic regression and maximum entropy models (2011) — 411 citations
• Scalable Coordinate Descent Approaches to Parallel Matrix Factorization for Recommender Systems (2012) — 294 citations
• PASSCoDe: Parallel ASynchronous Stochastic dual Co-ordinate Descent (2015) — 109 citations
• Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting (2019) — 473 citations
• Learning to Encode Position for Transformer with Continuous Dynamical Model (2020) — 141 citations
• Temporal Regularized Matrix Factorization for High-dimensional Time Series Prediction (2016) — 544 citations
• Collaborative Filtering with Graph Information: Consistency and Scalable Methods (2015) — 302 citations
• An Ensemble of Three Classifiers for KDD Cup 2009: Expanded Linear Model, Heterogeneous Boosting, and Selective Naive Bayes (2009) — 17 citations