Christopher Vendome
Artificial Intelligence · 2015–2016
A researcher in the Science corpus: 2 papers; trusts 52 researchers and is trusted by 30 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 42 citations into it; #13,853 of 35,345 by trust here, #14,718 by citations
- Information Systems — 36 citations into it; #872 of 8,771 by trust here, #1,142 by citations
- Natural Language Processing — 36 citations into it; #2,575 of 5,688 by trust here, #3,107 by citations
- Software — 23 citations into it; #276 of 1,227 by trust here, #260 by citations
- Signal Processing — 17 citations into it; #2,341 of 7,572 by trust here, #2,001 by citations
- Large Language Models — 16 citations into it
- Representation & Self-Supervised Learning — 14 citations into it
- Computer Vision and Pattern Recognition — 13 citations into it
Whom Christopher Vendome trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yoshua Bengio
- Geoffrey E. Hinton
- Tomas Mikolov
- Ilya Sutskever
- J. Urgen Schmidhuber
- Yann LeCun
- Richard Socher
- Alex Krizhevsky
- Andriy Mnih
- Diederik P. Kingma
Who trusts Christopher Vendome overall
- Vincent J. Hellendoorn
- Rafael-Michael Karampatsis
- Xiaodong Gu
- Michele Tufano
- Thibaud Lutellier
- Charles Sutton
- Hongyu Zhang
- Wei Fu
- Premkumar T. Devanbu
- Jian Zhang
Papers from Christopher Vendome's perspective
Ordered by how strongly the researchers Christopher Vendome 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)
- 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
- 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
- Toward Deep Learning Software Repositories (2015) — 323 citations
- Deep learning code fragments for code clone detection (2016) — 690 citations