Kfir Yehuda Levy
Artificial Intelligence · 2015–2018
A researcher in the Science corpus: 7 papers; trusts 157 researchers and is trusted by 65 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 68 citations into it; #3,234 of 35,345 by trust here, #10,667 by citations
- Computer Vision and Pattern Recognition — 54 citations into it
- Generative Models — 53 citations into it
- Optimization & Training — 45 citations into it; #1,554 of 2,601 by trust here, #2,378 by citations
- Computational Mechanics — 26 citations into it; #504 of 10,001 by trust here, #1,745 by citations
- Statistical and Nonlinear Physics — 14 citations into it
- Management Science and Operations Research — 13 citations into it; #3,696 of 11,946 by trust here, #3,705 by citations
- Signal Processing — 12 citations into it
Whom Kfir Yehuda Levy trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yoshua Bengio
- Yurii E. Nesterov
- Geoffrey E. Hinton
- Ilya Sutskever
- Alex Krizhevsky
- Rong Ge
- Richard S. Sutton
- Tamara G. Kolda
- Yann LeCun
- Diederik P. Kingma
Who trusts Kfir Yehuda Levy overall
- Simon Shaolei Du
- Esteban Real
- M. Soltanolkotabi
- Adams Wei Yu
- Jason D. Lee
- Chi Jin
- Hadi Daneshmand
- Jingzhao Zhang
- Zi-Mei Yao
- Fangyu Zou
Papers from Kfir Yehuda Levy's perspective
Ordered by how strongly the researchers Kfir Yehuda Levy trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- On Graduated Optimization for Stochastic Non-Convex Problems (2015) — 127 citations
- The Power of Normalization: Faster Evasion of Saddle Points (2016) — 115 citations
- Beyond Convexity: Stochastic Quasi-Convex Optimization (2015) — 207 citations
- Online Adaptive Methods, Universality and Acceleration (2018) — 110 citations
- Evaluating GANs via Duality (2018) — 8 citations
- A Domain Agnostic Measure for Monitoring and Evaluating GANs (2018) — 41 citations
- An Online Learning Approach to Generative Adversarial Networks (2017) — 92 citations