Roi Livni

Artificial Intelligence · 2012–2019

A researcher in the Science corpus: 4 papers; trusts 27 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 32 citations into it; #7,030 of 35,345 by trust here, #8,365 by citations
• Computer Vision and Pattern Recognition — 17 citations into it; #5,584 of 21,575 by trust here, #5,987 by citations
• Statistical Learning Theory — 16 citations into it
• Optimization & Training — 14 citations into it; #805 of 2,601 by trust here, #787 by citations
• Representation & Self-Supervised Learning — 13 citations into it
• Computational Mechanics — 6 citations into it
• Natural Language Processing — 5 citations into it

Whom Roi Livni trusts overall

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

1. Shai Shalev-Shwartz
2. Ilya Sutskever
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Jason Aaron Edward Weston
6. Yoshua Bengio
7. Yann LeCun
8. Navneet Dalal
9. Koby Crammer
10. Adam R. Klivans

Who trusts Roi Livni overall

1. Yuanzhi Li
2. Jason D. Lee
3. Zeyuan Allen-Zhu
4. Amit Daniely
5. Colin Wei
6. Tengyu Ma
7. Simon Shaolei Du
8. Rong Ge
9. Hassan Takabi
10. Yu Bai

Papers from Roi Livni's perspective

Ordered by how strongly the researchers Roi Livni 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

• On the Computational Efficiency of Training Neural Networks (2014) — 501 citations
• Generalize Across Tasks: Efficient Algorithms for Linear Representation Learning (2019) — 21 citations
• Improper Deep Kernels (2016) — 10 citations
• A Simple Geometric Interpretation of SVM using Stochastic Adversaries (2012) — 17 citations