David Charte
Artificial Intelligence · 2018–2020
A researcher in the Science corpus: 2 papers; trusts 43 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 72 citations into it
- Computer Vision and Pattern Recognition — 61 citations into it
- Representation & Self-Supervised Learning — 26 citations into it
- Signal Processing — 19 citations into it
- Generative Models — 15 citations into it
- Optimization & Training — 13 citations into it
- Media Technology — 6 citations into it
- Natural Language Processing — 6 citations into it
Whom David Charte trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Yoshua Bengio
- Yann LeCun
- Diederik P. Kingma
- J. Urgen Schmidhuber
- Alex Krizhevsky
- Pascal Vincent
- Ruslan Salakhutdinov
- R. Fisher
- Marc'Aurelio Ranzato
Who trusts David Charte overall
Papers from David Charte's perspective
Ordered by how strongly the researchers David Charte 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)