Yeming Wen

Artificial Intelligence · 2018–2022

A researcher in the Science corpus: 7 papers; trusts 317 researchers and is trusted by 25 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 237 citations into it; #7,462 of 35,345 by trust here, #6,614 by citations
• Computer Vision and Pattern Recognition — 102 citations into it; #5,586 of 21,575 by trust here, #6,737 by citations
• Optimization & Training — 102 citations into it; #2,300 of 2,601 by trust here, #1,585 by citations
• Reinforcement Learning — 47 citations into it
• Probabilistic & Bayesian Methods — 33 citations into it
• Computational Mechanics — 29 citations into it; #2,237 of 10,001 by trust here, #2,782 by citations
• Evaluation & Benchmarks — 28 citations into it
• Model Architectures — 24 citations into it

Whom Yeming Wen trusts overall

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

1. Alex Krizhevsky
2. L. Eon Bottou
3. Geoffrey E. Hinton
4. Yann LeCun
5. Diederik P. Kingma
6. James Martens
7. Yoshua Bengio
8. Kaiming He
9. Ilya Sutskever
10. Radford M. Neal

Who trusts Yeming Wen overall

1. Fei Ye
2. Dustin Tran
3. Michael W. Dusenberry
4. Mitchell Wortsman
5. Matthew Wallingford
6. Adrian G. Bors
7. Rahul Ramesh
8. Danijar Hafner
9. Florian Wenzel
10. Jasper Snoek

Papers from Yeming Wen's perspective

Ordered by how strongly the researchers Yeming Wen 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. 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Benchmarking Model-Based Reinforcement Learning (2019) — 394 citations
• BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning (2020) — 586 citations
• Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches (2018) — 353 citations
• Natural Language to Code Generation in Interactive Data Science Notebooks (2022) — 122 citations
• Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors (2020) — 240 citations
• An Empirical Study of Large-Batch Stochastic Gradient Descent with Structured Covariance Noise (2019) — 24 citations
• Interplay Between Optimization and Generalization of Stochastic Gradient Descent with Covariance Noise (2019) — 27 citations