Gregory F. Coppola
Artificial Intelligence · 2015
A researcher in the Science corpus: 1 paper; trusts 114 researchers and is trusted by 19 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 50 citations into it
- Optimization & Training — 31 citations into it; #763 of 2,601 by trust here, #2,162 by citations
- Natural Language Processing — 25 citations into it; #1,585 of 5,688 by trust here, #4,793 by citations
- Computational Mechanics — 10 citations into it
- Management Science and Operations Research — 10 citations into it
- Computer Vision and Pattern Recognition — 8 citations into it
- Representation & Self-Supervised Learning — 8 citations into it
- Computer Networks and Communications — 7 citations into it
Whom Gregory F. Coppola trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Michael Collins
- Ryan T. McDonald
- Yurii E. Nesterov
- John C. Duchi
- Dimitri P. Bertsekas
- David Chiang
- Mark Johnson
- Fernando C Pereira
- Franz Joseph Och
- Vladimir Naumovich Vapni
Who trusts Gregory F. Coppola overall
- Ahmed Khaled
- Sebastian U. Stich
- Anastasia Koloskova
- Xiang Li
- Konstantin Mishchenko
- Peter Richtárik
- Nicolas Loizou
- Kaixuan Huang
- Navjot Singh
- Dmitrii Avdiukhin
Papers from Gregory F. Coppola's perspective
Ordered by how strongly the researchers Gregory F. Coppola trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)