Uri Shaham

Artificial Intelligence · with Kluger · 2015–2018

A researcher in the Science corpus: 7 papers; trusts 188 researchers and is trusted by 24 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 56 citations into it
• Computer Vision and Pattern Recognition — 47 citations into it
• Optimization & Training — 31 citations into it; #895 of 2,601 by trust here, #988 by citations
• Representation & Self-Supervised Learning — 30 citations into it
• Statistics and Probability — 23 citations into it
• Safety, Robustness & Interpretability — 20 citations into it; #574 of 2,251 by trust here, #930 by citations
• Model Architectures — 9 citations into it
• Computational Mechanics — 8 citations into it

Whom Uri Shaham trusts overall

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

1. Yoshua Bengio
2. Alex Krizhevsky
3. Yann LeCun
4. Geoffrey E. Hinton
5. Yurii E. Nesterov
6. Nitish Srivastava
7. Kaiming He
8. Diederik P. Kingma
9. Pascal Vincent
10. Ilya Sutskever

Who trusts Uri Shaham overall

1. Moustapha Cissé
2. Pierre Stock
3. Nicholas Carlini
4. Jason Jo
5. Xin Li
6. Sungyeob Han
7. A. Serban
8. Michihiro Yasunaga
9. Dong Yin
10. Claudio Gambella

Papers from Uri Shaham's perspective

Ordered by how strongly the researchers Uri Shaham 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• SpectralNet: Spectral Clustering using Deep Neural Networks (2018) — 330 citations
• Removal of batch effects using distribution‐matching residual networks (2016) — 177 citations
• DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network (2016) — 1,801 citations
• Learning by coincidence: Siamese networks and common variable learning (2018) — 38 citations
• Deep Survival : The Deep Cox Proportional Hazards Network (2016)
• Understanding Adversarial Training: Increasing Local Stability of Neural Nets through Robust Optimization (2015) — 378 citations
• Understanding adversarial training: Increasing local stability of supervised models through robust optimization (2015) — 104 citations