Christopher DuBois
Artificial Intelligence · 2010–2020
A researcher in the Science corpus: 4 papers; trusts 87 researchers and is trusted by 16 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 63 citations into it
- Natural Language Processing — 42 citations into it
- Computer Vision and Pattern Recognition — 33 citations into it; #7,330 of 21,575 by trust here, #6,737 by citations
- Information Systems — 20 citations into it; #1,815 of 8,771 by trust here, #1,867 by citations
- Evaluation & Benchmarks — 18 citations into it
- Recommender Systems — 14 citations into it; #95 of 233 by trust here, #100 by citations
- Management Science and Operations Research — 11 citations into it
- Information Retrieval & Search — 8 citations into it
Whom Christopher DuBois trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Tom A. B. Snijders
- Krzysztof Nowicki
- T. Griffiths
- Geoffrey E. Hinton
- Yoshua Bengio
- Yehuda Koren
- David M. Blei
- S. Geisser
- Gary King
- Ruslan Salakhutdinov
Who trusts Christopher DuBois overall
- Xiangnan He
- Yongfeng Zhang
- Charu C. Aggarwal
- Vito Bellini
- Aminu Da’u
- Jiaxi Tang
- P. Li
- Xiang Wang
- Dawen Liang
- Xu Chen
Papers from Christopher DuBois's perspective
Ordered by how strongly the researchers Christopher DuBois 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)
- Attention is All you Need (Neural Information Processing Systems)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- How Effective is Task-Agnostic Data Augmentation for Pretrained Transformers? (2020) — 94 citations
- An Exploration of Data Augmentation and Sampling Techniques for Domain-Agnostic Question Answering (2019) — 74 citations
- Collaborative Denoising Auto-Encoders for Top-N Recommender Systems (2016) — 1,053 citations
- Modeling relational events via latent classes (2010) — 35 citations