James McInerney

Artificial Intelligence · 2014–2018

A researcher in the Science corpus: 6 papers; trusts 41 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 41 citations into it
• Information Systems — 26 citations into it; #8,297 of 8,771 by trust here, #4,012 by citations
• Management Science and Operations Research — 25 citations into it; #11,565 of 11,946 by trust here, #4,746 by citations
• Probabilistic & Bayesian Methods — 22 citations into it
• Recommender Systems — 19 citations into it
• Statistics and Probability — 19 citations into it
• Computer Vision and Pattern Recognition — 13 citations into it
• Optimization & Training — 10 citations into it

Whom James McInerney trusts overall

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

1. Michael I. Jordan
2. David M. Blei
3. L. Eon Bottou
4. Matthew D. Hoffman
5. Radford M. Neal
6. Diederik P. Kingma
7. Geoffrey E. Hinton
8. Zoubin Ghahramani
9. Andrew Y. Ng
10. Donald B. Rubin

Who trusts James McInerney overall

1. Jason Ramapuram
2. Jihong Park
3. Cheng Zhang
4. Magda Gregorová
5. Xiangnan He

Papers from James McInerney's perspective

Ordered by how strongly the researchers James McInerney 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. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
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

• Modeling User Exposure in Recommendation (2015) — 426 citations
• Variational Tempering (2014) — 61 citations
• Explore, exploit, and explain: personalizing explainable recommendations with bandits (2018) — 185 citations
• Towards a Fair Marketplace: Counterfactual Evaluation of the trade-off between Relevance, Fairness & Satisfaction in Recommendation Systems (2018) — 323 citations
• The Population Posterior and Bayesian Modeling on Streams (2015) — 35 citations
• Multicanonical Stochastic Variational Inference (2014)