Alberto Bietti

Artificial Intelligence · 2017–2019

A researcher in the Science corpus: 7 papers; trusts 432 researchers and is trusted by 26 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 162 citations into it; #7,472 of 35,345 by trust here, #17,776 by citations
• Computer Vision and Pattern Recognition — 86 citations into it; #5,055 of 21,575 by trust here, #9,722 by citations
• Statistical Learning Theory — 47 citations into it; #372 of 1,038 by trust here, #635 by citations
• Model Architectures — 36 citations into it; #634 of 2,278 by trust here, #1,620 by citations
• Optimization & Training — 35 citations into it
• Scaling & Learning Theory — 35 citations into it
• Safety, Robustness & Interpretability — 30 citations into it
• Management Science and Operations Research — 25 citations into it; #9,929 of 11,946 by trust here, #11,360 by citations

Whom Alberto Bietti trusts overall

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

1. Stéphane Mallat
2. Geoffrey E. Hinton
3. Kaiming He
4. Alex Krizhevsky
5. Francis R. Bach
6. Yoshua Bengio
7. Julien Mairal
8. Yann LeCun
9. J. Langford
10. Diederik P. Kingma

Who trusts Alberto Bietti overall

1. Ronen Basri
2. Julien Mairal
3. Aleksandrs Slivkins
4. Amnon Geifman
5. Yu Bai
6. Lin Chen
7. Soufiane Hayou
8. Yassine Yaakoubi
9. Alessandro Favero
10. Yuval Belfer

Papers from Alberto Bietti's perspective

Ordered by how strongly the researchers Alberto Bietti 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. 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

• On the Inductive Bias of Neural Tangent Kernels (2019) — 316 citations
• Invariance and Stability of Deep Convolutional Representations (2017) — 41 citations
• Group Invariance, Stability to Deformations, and Complexity of Deep Convolutional Representations (2017) — 98 citations
• A Contextual Bandit Bake-off (2018) — 128 citations
• On Regularization and Robustness of Deep Neural Networks (2018) — 15 citations
• Practical Evaluation and Optimization of Contextual Bandit Algorithms (2018) — 27 citations
• A Kernel Perspective for Regularizing Deep Neural Networks (2018) — 74 citations