Yi-An Ma

Artificial Intelligence · with Chen · 2015–2020

A researcher in the Science corpus: 3 papers; trusts 17 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 119 citations into it
• Computer Vision and Pattern Recognition — 47 citations into it
• Probabilistic & Bayesian Methods — 22 citations into it
• Statistics and Probability — 21 citations into it; #4,564 of 12,932 by trust here, #3,330 by citations
• Optimization & Training — 18 citations into it
• Statistical and Nonlinear Physics — 7 citations into it

Whom Yi-An Ma trusts overall

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

1. Radford M. Neal
2. Max Welling
3. Geoffrey E. Hinton
4. Michael I. Jordan
5. Diederik P. Kingma
6. Michel Le Bellac
7. Yee Whye Teh
8. Alex Krizhevsky
9. Kaiming He
10. Yoshua Bengio

Who trusts Yi-An Ma overall

1. Stephan Mandt
2. Javier Antorán Cabiscol
3. Zhenyi Wang
4. Anqi Wu
5. Pratik Chaudhari
6. Christopher Nemeth
7. Matthew D. Hoffman
8. Yunfu Song
9. Fredrik K. Gustafsson
10. Liu Yang

Papers from Yi-An Ma's perspective

Ordered by how strongly the researchers Yi-An Ma 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. ImageNet: A large-scale hierarchical image database
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. Learning Multiple Layers of Features from Tiny Images
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Underspecification Presents Challenges for Credibility in Modern Machine Learning (2020) — 897 citations
• Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors (2020) — 240 citations
• A Complete Recipe for Stochastic Gradient MCMC (2015) — 537 citations