Florian Bordes

Computer Vision and Pattern Recognition · 2017–2023

A researcher in the Science corpus: 4 papers; trusts 592 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 259 citations into it
• Artificial Intelligence — 230 citations into it
• Representation & Self-Supervised Learning — 140 citations into it
• Generative Models — 58 citations into it; #837 of 1,956 by trust here, #1,601 by citations
• Model Architectures — 35 citations into it
• Optimization & Training — 27 citations into it
• Signal Processing — 23 citations into it
• Probabilistic & Bayesian Methods — 21 citations into it

Whom Florian Bordes trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Yoshua Bengio
4. Kaiming He
5. Diederik P. Kingma
6. Yann LeCun
7. Ruslan Salakhutdinov
8. Ilya Sutskever
9. Christian Szegedy
10. Ross B. Girshick

Who trusts Florian Bordes overall

1. Jiaming Song
2. Shengjia Zhao
3. Yang Song
4. Stefano Ermon
5. Qinsheng Zhang
6. Piotr Bojanowski
7. Jonathan Ho
8. Mahmoud Assran
9. Yongxin Chen
10. Armand Joulin

Papers from Florian Bordes's perspective

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

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

Their papers

• Masked Siamese Networks for Label-Efficient Learning (2022) — 424 citations
• High Fidelity Visualization of What Your Self-Supervised Representation Knows About (2021) — 79 citations
• A Cookbook of Self-Supervised Learning (2023) — 398 citations
• Learning to Generate Samples from Noise through Infusion Training (2017) — 44 citations