Yaoliang Yu

Artificial Intelligence · 2012–2021

A researcher in the Science corpus: 12 papers; trusts 352 researchers and is trusted by 38 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 165 citations into it; #5,485 of 35,345 by trust here, #6,796 by citations
• Computer Vision and Pattern Recognition — 96 citations into it; #5,618 of 21,575 by trust here, #5,116 by citations
• Statistics and Probability — 53 citations into it; #6,723 of 12,932 by trust here, #11,487 by citations
• Computational Mechanics — 33 citations into it
• Natural Language Processing — 30 citations into it; #4,700 of 5,688 by trust here, #4,983 by citations
• Statistical Learning Theory — 29 citations into it
• Optimization & Training — 29 citations into it
• Safety, Robustness & Interpretability — 28 citations into it

Whom Yaoliang Yu trusts overall

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

1. Christopher K. I. Williams
2. Yurii E. Nesterov
3. Robert Tibshirani
4. David G. Lowe
5. Geoffrey E. Hinton
6. Trevor J. Hastie
7. Alex Krizhevsky
8. Yoshua Bengio
9. Diederik P. Kingma
10. Karen Simonyan

Who trusts Yaoliang Yu overall

1. Wouter M. Kouw
2. Junming Yin
3. Corinna Cortes
4. Canwen Xu
5. Arun Shankar Iyer
6. Mingming Gong
7. Zirui Wang
8. Ritambhara Singh
9. Kirthevasan Kandasamy
10. Kun Kuang

Papers from Yaoliang Yu's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. 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

• Analysis of Kernel Mean Matching under Covariate Shift (2012) — 97 citations
• Petuum: A New Platform for Distributed Machine Learning on Big Data (2013) — 526 citations
• DeeBERT: Dynamic Early Exiting for Accelerating BERT Inference (2020) — 534 citations
• Early Exiting BERT for Efficient Document Ranking (2020) — 49 citations
• Semantic Pooling for Complex Event Analysis in Untrimmed Videos (2016) — 326 citations
• BERxiT: Early Exiting for BERT with Better Fine-Tuning and Extension to Regression (2021) — 165 citations
• Additive Approximations in High Dimensional Nonparametric Regression via the SALSA (2016) — 47 citations
• Are My Deep Learning Systems Fair? An Empirical Study of Fixed-Seed Training (2021) — 49 citations
• Distributional Reinforcement Learning for Efficient Exploration (2019) — 107 citations
• Convex-constrained Sparse Additive Modeling and Its Extensions (2017) — 3 citations
• On Convergence of Model Parallel Proximal Gradient Algorithm for Stale Synchronous Parallel System (2016) — 30 citations
• On Decomposing the Proximal Map (2013) — 107 citations