Aristide Baratin

Artificial Intelligence · 2011–2020

A researcher in the Science corpus: 7 papers; trusts 153 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 155 citations into it; #30,259 of 35,345 by trust here, #6,330 by citations
• Computer Vision and Pattern Recognition — 85 citations into it
• Scaling & Learning Theory — 65 citations into it; #385 of 428 by trust here, #205 by citations
• Statistical and Nonlinear Physics — 58 citations into it; #4,048 of 10,127 by trust here, #1,486 by citations
• Optimization & Training — 48 citations into it
• Generative Models — 38 citations into it
• Model Architectures — 25 citations into it
• Statistics and Probability — 24 citations into it

Whom Aristide Baratin trusts overall

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

1. Alex Krizhevsky
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. Kaiming He
6. Yann LeCun
7. Karen Simonyan
8. Peter L. Bartlett
9. Christian Szegedy
10. L. Freidel

Who trusts Aristide Baratin overall

1. Alejandro Perez
2. D. Oriti
3. Yoshua Bengio
4. Jiaming Song
5. Zhi-Qin John Xu
6. David A. McAllester
7. Xingchao Peng
8. Ben Poole
9. Lorenzo Sindoni
10. Adar Elad

Papers from Aristide Baratin's perspective

Ordered by how strongly the researchers Aristide Baratin 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

• Mutual Information Neural Estimation (2018) — 1,674 citations
• On the Spectral Bias of Neural Networks (2018) — 2,472 citations
• A Modern Take on the Bias-Variance Tradeoff in Neural Networks (2018) — 186 citations
• A3T: Adversarially Augmented Adversarial Training (2018) — 9 citations
• Implicit Regularization via Neural Feature Alignment (2020) — 71 citations
• MINE: Mutual Information Neural Estimation (2018) — 747 citations
• Group field theory and simplicial gravity path integrals: A model for Holst-Plebanski gravity (2011) — 146 citations