Mikhail Yurochkin

Artificial Intelligence · with Agarwal · 2018–2020

A researcher in the Science corpus: 6 papers; trusts 19 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 56 citations into it; #9,375 of 35,345 by trust here, #6,720 by citations
• Optimization & Training — 20 citations into it; #1,677 of 2,601 by trust here, #1,120 by citations
• Federated & Distributed Learning — 16 citations into it; #88 of 319 by trust here, #79 by citations
• Computer Vision and Pattern Recognition — 8 citations into it
• Evaluation & Benchmarks — 6 citations into it
• Probabilistic & Bayesian Methods — 6 citations into it; #1,372 of 1,525 by trust here, #601 by citations
• Model Architectures — 5 citations into it
• Statistics and Probability — 5 citations into it; #10,873 of 12,932 by trust here, #2,693 by citations

Whom Mikhail Yurochkin trusts overall

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

1. Michael I. Jordan
2. David M. Blei
3. T. Griffiths
4. Romain Thibaux
5. Chong Wang
6. Geoffrey E. Hinton
7. Yee Whye Teh
8. Radford M. Neal
9. Zoubin Ghahramani
10. Yann LeCun

Who trusts Mikhail Yurochkin overall

1. Qinbin Li
2. Hongyi Wang
3. Jaehong Yoon
4. Chaoyang He
5. Hong-You Chen
6. Guodong Long
7. Bingsheng He
8. Yutao Huang
9. Wonyong Jeong
10. Tzu-Ming Harry Hsu

Papers from Mikhail Yurochkin's perspective

Ordered by how strongly the researchers Mikhail Yurochkin 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Federated Learning with Matched Averaging (2020) — 1,404 citations
• Bayesian Nonparametric Federated Learning of Neural Networks (2019) — 912 citations
• IBM Federated Learning: an Enterprise Framework White Paper V0.1 (2020) — 198 citations
• Statistical Model Aggregation via Parameter Matching (2019) — 38 citations
• Streaming dynamic and distributed inference of latent geometric structures (2018) — 3 citations
• Scalable inference of topic evolution via models for latent geometric structures (2018) — 12 citations