Mark D. Reid
Artificial Intelligence · 2009–2016
A researcher in the Science corpus: 6 papers; trusts 66 researchers and is trusted by 21 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 62 citations into it; #15,012 of 35,345 by trust here, #20,377 by citations
- Statistics and Probability — 44 citations into it
- Management Science and Operations Research — 25 citations into it; #6,801 of 11,946 by trust here, #7,262 by citations
- Statistical Learning Theory — 22 citations into it; #521 of 1,038 by trust here, #531 by citations
- Computer Vision and Pattern Recognition — 19 citations into it
- Probabilistic & Bayesian Methods — 16 citations into it; #1,196 of 1,525 by trust here, #1,313 by citations
- Optimization & Training — 15 citations into it; #1,753 of 2,601 by trust here, #2,215 by citations
- Applied Mathematics — 12 citations into it
Whom Mark D. Reid trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Peter L. Bartlett
- Vladimir Naumovich Vapnik
- Alex Smola
- Michael I. Jordan
- Bernhard Schölkopf
- Tom Fawcett
- Donald Ervin Knuth
- Eric R. Ziegel
- Tong Zhang
- Arthur Gretton
Who trusts Mark D. Reid overall
- Shivani Agarwal
- Balaji Lakshminarayanan
- Robert C. Williamson
- Clayton D. Scott
- Giorgio Patrini
- Shakir Mohamed
- Marco Loog
- Masatoshi Uehara
- Igal Sason
- Davood Karimi
Papers from Mark D. Reid's perspective
Ordered by how strongly the researchers Mark D. Reid 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)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- 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)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Causal Bandits: Learning Good Interventions via Causal Inference (2016) — 197 citations
- A Hybrid Loss for Multiclass and Structured Prediction (2014) — 7 citations
- Tighter Variational Representations of f-Divergences via Restriction to Probability Measures (2012) — 69 citations
- Composite Multiclass Losses (2011) — 99 citations
- Information, Divergence and Risk for Binary Experiments (2009) — 250 citations
- Composite Binary Losses (2009) — 239 citations