Hugo Berard

Artificial Intelligence · 2017–2021

A researcher in the Science corpus: 7 papers; trusts 243 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 179 citations into it
• Optimization & Training — 166 citations into it
• Generative Models — 86 citations into it; #565 of 1,956 by trust here, #859 by citations
• Computer Vision and Pattern Recognition — 85 citations into it; #7,951 of 21,575 by trust here, #9,435 by citations
• Computational Mechanics — 61 citations into it; #2,945 of 10,001 by trust here, #3,029 by citations
• Management Science and Operations Research — 43 citations into it
• Computational Theory and Mathematics — 30 citations into it
• Statistics and Probability — 29 citations into it

Whom Hugo Berard trusts overall

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

1. Alex Krizhevsky
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Yurii E. Nesterov
5. Yoshua Bengio
6. Ian J. Goodfellow
7. Yann LeCun
8. Lars M. Mescheder
9. L. Eon Bottou
10. Kaiming He

Who trusts Hugo Berard overall

1. Gauthier Gidel
2. Waïss Azizian
3. Simon Lacoste-Julien
4. Ioannis Mitliagkas
5. Aryan Mokhtari
6. Yu-Guan Hsieh
7. Tatjana Chavdarova
8. Yasin Yazici
9. Lars M. Mescheder
10. Alina Ene

Papers from Hugo Berard's perspective

Ordered by how strongly the researchers Hugo Berard 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. 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. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• A Variational Inequality Perspective on Generative Adversarial Networks (2018) — 409 citations
• A Closer Look at the Optimization Landscapes of Generative Adversarial Networks (2019) — 66 citations
• Online Adversarial Attacks (2021) — 13 citations
• Stochastic Gradient Descent-Ascent and Consensus Optimization for Smooth Games: Convergence Analysis under Expected Co-coercivity (2021) — 64 citations
• Stochastic Hamiltonian Gradient Methods for Smooth Games (2020) — 55 citations
• A Variational Inequality Perspective on Generative Adversarial Nets (2018) — 32 citations
• Parametric Adversarial Divergences are Good Task Losses for Generative Modeling (2017) — 13 citations