Félix G. Harvey

Artificial Intelligence · 2015–2020

A researcher in the Science corpus: 3 papers; trusts 148 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 43 citations into it
• Computer Vision and Pattern Recognition — 40 citations into it
• Reinforcement Learning — 26 citations into it
• Control and Systems Engineering — 18 citations into it
• Model Architectures — 16 citations into it
• Natural Language Processing — 10 citations into it
• Representation & Self-Supervised Learning — 8 citations into it
• Biomedical Engineering — 7 citations into it

Whom Félix G. Harvey trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Ilya Sutskever
4. Alex Graves
5. Sepp Hochreiter
6. Kyunghyun Cho
7. J. Urgen Schmidhuber
8. Alex Krizhevsky
9. Diederik P. Kingma
10. Yann LeCun

Who trusts Félix G. Harvey overall

1. Guy Tevet
2. Mathis Petrovich

Papers from Félix G. Harvey's perspective

Ordered by how strongly the researchers Félix G. Harvey trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Robust motion in-betweening (2020) — 396 citations
• Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning (2019) — 24 citations
• Semi-supervised Learning with Encoder-Decoder Recurrent Neural Networks: Experiments with Motion Capture Sequences (2015) — 10 citations