Lior Deutsch

Artificial Intelligence · 2019

A researcher in the Science corpus: 1 paper; trusts 54 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 24 citations into it
• Optimization & Training — 14 citations into it
• Computer Vision and Pattern Recognition — 12 citations into it
• Probabilistic & Bayesian Methods — 11 citations into it
• Model Architectures — 9 citations into it

Whom Lior Deutsch trusts overall

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

1. Diederik P. Kingma
2. Alex Graves
3. Yoshua Bengio
4. Geoffrey E. Hinton
5. Christian Szegedy
6. Kaiming He
7. Ian J. Goodfellow
8. Yann LeCun
9. Alex Krizhevsky
10. J. Urgen Schmidhuber

Papers from Lior Deutsch's perspective

Ordered by how strongly the researchers Lior Deutsch 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. 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 Generative Model for Sampling High-Performance and Diverse Weights for Neural Networks (2019) — 16 citations