Daniel Lévy

Artificial Intelligence · 2017

A researcher in the Science corpus: 2 papers; trusts 57 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 44 citations into it; #29,854 of 35,345 by trust here, #16,166 by citations
• Probabilistic & Bayesian Methods — 42 citations into it; #1,123 of 1,525 by trust here, #1,113 by citations
• Computer Vision and Pattern Recognition — 21 citations into it; #18,347 of 21,575 by trust here, #10,905 by citations
• Statistics and Probability — 17 citations into it
• Natural Language Processing — 16 citations into it; #4,869 of 5,688 by trust here, #3,332 by citations
• Model Architectures — 13 citations into it
• Generative Models — 12 citations into it
• Optimization & Training — 8 citations into it

Whom Daniel Lévy trusts overall

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

1. Radford M. Neal
2. Jascha Narain Sohl-Dickstein
3. Diederik P. Kingma
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Charles H. Bennett
7. Michael Betancourt
8. Alex Krizhevsky
9. Yann LeCun
10. Ilya Sutskever

Who trusts Daniel Lévy overall

1. Yang Song
2. Jiaming Song
3. Chenlin Meng
4. Peter Toth
5. Matthew D. Hoffman
6. Bo Dai
7. Stefano Ermon
8. Akshay Agrawal
9. Danilo Jimenez Rezende
10. Pavel Sountsov

Papers from Daniel Lévy's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
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. GENERATIVE ADVERSARIAL NETS
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Data Noising as Smoothing in Neural Network Language Models (2017) — 260 citations
• Generalizing Hamiltonian Monte Carlo with Neural Networks (2017) — 136 citations