Tom Véniat

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 3 papers; trusts 396 researchers and is trusted by 34 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 60 citations into it; #6,422 of 35,345 by trust here, #11,750 by citations
• Computer Vision and Pattern Recognition — 60 citations into it; #5,938 of 21,575 by trust here, #12,172 by citations
• Model Architectures — 27 citations into it; #1,134 of 2,278 by trust here, #1,826 by citations
• Efficiency & Compression — 19 citations into it; #562 of 1,264 by trust here, #1,050 by citations
• Optimization & Training — 18 citations into it
• Meta & Continual Learning — 13 citations into it; #390 of 1,041 by trust here, #660 by citations
• Representation & Self-Supervised Learning — 13 citations into it
• Reinforcement Learning — 9 citations into it

Whom Tom Véniat trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Kaiming He
4. Yann LeCun
5. Yoshua Bengio
6. Diederik P. Kingma
7. Ilya Sutskever
8. Ronald J. Williams
9. Christian Szegedy
10. Marc'Aurelio Ranzato

Who trusts Tom Véniat overall

1. Carl Lemaire
2. Mark Sandler
3. Oleksiy Ostapenko
4. Sayna Ebrahimi
5. Sen Lin
6. Li Yang
7. Andrew Achkar
8. Roy Schwartz
9. Andrew G. Howard
10. Bichen Wu

Papers from Tom Véniat's perspective

Ordered by how strongly the researchers Tom Véniat 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
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. Going deeper with convolutions

Their papers

• Learning Time/Memory-Efficient Deep Architectures with Budgeted Super Networks (2017) — 84 citations
• Learning Time-Efficient Deep Architectures with Budgeted Super Networks (2017) — 22 citations
• Efficient Continual Learning with Modular Networks and Task-Driven Priors (2020) — 115 citations