Allison June-Barlow Chaney

Information Systems · 2015–2017

A researcher in the Science corpus: 2 papers; trusts 44 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Information Systems — 56 citations into it; #6,381 of 8,771 by trust here, #4,518 by citations
• Artificial Intelligence — 25 citations into it; #32,228 of 35,345 by trust here, #29,412 by citations
• Management Science and Operations Research — 21 citations into it
• Recommender Systems — 18 citations into it
• Computer Vision and Pattern Recognition — 15 citations into it
• Marketing — 15 citations into it
• Statistical and Nonlinear Physics — 15 citations into it
• Computer Networks and Communications — 12 citations into it

Whom Allison June-Barlow Chaney trusts overall

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

1. Yehuda Koren
2. David M. Blei
3. Hao Ma
4. John F. Canny
5. Ruslan Salakhutdinov
6. Robert M. Bell
7. Michael I. Jordan
8. Ian T. Jolliffe
9. Òscar Celma
10. Geoffrey E. Hinton

Who trusts Allison June-Barlow Chaney overall

1. Ninareh Mehrabi
2. Masoud Mansoury
3. Yu Zheng
4. Jiawei Chen
5. Brandon M Stewart
6. Fred Morstatter
7. Himan Abdollahpouri
8. Chen Gao
9. Xiangnan He
10. Yongfeng Zhang

Papers from Allison June-Barlow Chaney's perspective

Ordered by how strongly the researchers Allison June-Barlow Chaney 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. Gradient-based learning applied to document recognition
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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• How algorithmic confounding in recommendation systems increases homogeneity and decreases utility (2017) — 397 citations
• A Probabilistic Model for Using Social Networks in Personalized Item Recommendation (2015) — 182 citations