Brian M. D’Alessandro
Artificial Intelligence · 2009–2017
A researcher in the Science corpus: 6 papers; trusts 17 researchers and is trusted by 2 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 42 citations into it
- Information Systems — 12 citations into it; #7,793 of 8,771 by trust here, #4,682 by citations
- Safety, Robustness & Interpretability — 10 citations into it
- Sociology and Political Science — 10 citations into it; #16,780 of 22,756 by trust here, #11,516 by citations
- AI Ethics, Fairness & Society — 9 citations into it
- Management Science and Operations Research — 8 citations into it
- Optimization & Training — 8 citations into it
- Safety Research — 7 citations into it
Whom Brian M. D’Alessandro trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Indrė Žliobaitė
- Faisal Kamiran
- Wei Xu
- Salvatore Ruggieri
- Andrea Romei
- Foster J. Provost
- Dino Pedreschi
- Sinno Jialin Pan
- Sara Hajian
- Toon Calders
Who trusts Brian M. D’Alessandro overall
- Claudia Perlich
- Ninareh Mehrabi
- Charu C. Aggarwal
- Foster J. Provost
- Fred Morstatter
- Josh Attenberg
- Weinan Zhang
Papers from Brian M. D’Alessandro's perspective
Ordered by how strongly the researchers Brian M. D’Alessandro 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)
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Audience selection for on-line brand advertising: privacy-friendly social network targeting (2009) — 152 citations
- Evaluating and Optimizing Online Advertising: Forget the Click, but There Are Good Proxies (2015) — 49 citations
- Scalable hands-free transfer learning for online advertising (2014) — 44 citations
- Conscientious Classification: A Data Scientist's Guide to Discrimination-Aware Classification (2017) — 201 citations
- Explaining Classification Models Built on High-Dimensional Sparse Data (2016) — 16 citations
- Machine learning for targeted display advertising: transfer learning in action (2013) — 179 citations