Robert M. Bell
Information Systems · 2005–2011
A researcher in the Science corpus: 7 papers; trusts 13 researchers and is trusted by 425 researchers here, as inferred from the citation graph.
Subfields
- Information Systems — 36 citations into it; #17 of 8,771 by trust here, #32 by citations
- Recommender Systems — 20 citations into it; #14 of 233 by trust here, #34 by citations
- Artificial Intelligence — 17 citations into it
- Computer Vision and Pattern Recognition — 13 citations into it; #2,342 of 21,575 by trust here, #1,370 by citations
- Management Science and Operations Research — 11 citations into it; #570 of 11,946 by trust here, #1,334 by citations
- Marketing — 7 citations into it; #15 of 6,289 by trust here, #30 by citations
- Probabilistic & Bayesian Methods — 7 citations into it
- Signal Processing — 6 citations into it
Whom Robert M. Bell trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yehuda Koren
- Ruslan Salakhutdinov
- Arkadiusz Paterek
- Andriy Mnih
- Badrul Munir Sarwar
- Geoffrey E. Hinton
- J. Konstan
- George Karypis
- C. Volinsky
- Gediminas Adomavicius
Who trusts Robert M. Bell overall
- Yehuda Koren
- Charu C. Aggarwal
- Julian McAuley
- Xiangnan He
- Ruining He
- Aleksandra Korolova
- Yongfeng Zhang
- Serhiy Morozov
- Noam Koenigstein
- Xin Luo
Papers from Robert M. Bell's perspective
Ordered by how strongly the researchers Robert M. Bell trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Matrix Factorization Techniques for Recommender Systems (2009) — 10,960 citations
- Modeling relationships at multiple scales to improve accuracy of large recommender systems (2007) — 411 citations
- Advances in Collaborative Filtering (2011) — 1,517 citations
- Lessons from the Netflix prize challenge (2007) — 848 citations
- All Together Now: A Perspective on the Netflix Prize (2010) — 88 citations
- The BellKor solution to the Netflix Prize (2007) — 239 citations
- Predicting the location and number of faults in large software systems (2005) — 753 citations