Pierre H. Richemond

Artificial Intelligence · 2017–2022

A researcher in the Science corpus: 5 papers; trusts 201 researchers and is trusted by 85 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 155 citations into it; #25,079 of 35,345 by trust here, #2,409 by citations
• Computer Vision and Pattern Recognition — 138 citations into it; #3,522 of 21,575 by trust here, #1,957 by citations
• Representation & Self-Supervised Learning — 58 citations into it; #3,011 of 3,145 by trust here, #337 by citations
• Generative Models — 43 citations into it
• Natural Language Processing — 29 citations into it
• Optimization & Training — 29 citations into it
• Statistical and Nonlinear Physics — 21 citations into it
• Reinforcement Learning — 20 citations into it

Whom Pierre H. Richemond trusts overall

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

1. Geoffrey E. Hinton
2. Kaiming He
3. Diederik P. Kingma
4. Alex Krizhevsky
5. Brian D. Ziebart
6. Yoshua Bengio
7. Cédric Villani
8. Sergey Ioffe
9. Ross B. Girshick
10. Yann LeCun

Who trusts Pierre H. Richemond overall

1. Adrien Bardes
2. Shantanu Thakoor
3. Haohang Xu
4. Xinlei Chen
5. Yann LeCun
6. Jean Ponce
7. Geoffrey E. Hinton
8. Kaiming He
9. Ishan Misra
10. Christoph Feichtenhofer

Papers from Pierre H. Richemond's perspective

Ordered by how strongly the researchers Pierre H. Richemond 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. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning (2020) — 8,797 citations
• BYOL works even without batch statistics (2020) — 131 citations
• Data Distributional Properties Drive Emergent In-Context Learning in Transformers (2022) — 389 citations
• Continuous diffusion for categorical data (2022) — 209 citations
• A short variational proof of equivalence between policy gradients and soft Q learning (2017) — 6 citations