Hilal Asi
Artificial Intelligence · 2018–2019
A researcher in the Science corpus: 3 papers; trusts 79 researchers and is trusted by 12 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 64 citations into it; #10,109 of 35,345 by trust here, #21,878 by citations
- Optimization & Training — 41 citations into it; #1,036 of 2,601 by trust here, #1,904 by citations
- Computational Mechanics — 25 citations into it; #2,366 of 10,001 by trust here, #5,350 by citations
- Statistics and Probability — 25 citations into it
- Computer Vision and Pattern Recognition — 16 citations into it
- Privacy & Security — 16 citations into it
- Management Science and Operations Research — 14 citations into it; #2,534 of 11,946 by trust here, #5,548 by citations
- Numerical Analysis — 11 citations into it
Whom Hilal Asi trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Cynthia Dwork
- Anatoli B. Juditsky
- Yann LeCun
- Tong Zhang
- John C. Duchi
- Guanghui Lan
- Deanna Needell
- Geoffrey E. Hinton
- L. Eon Bottou
Who trusts Hilal Asi overall
- Antonio Orvieto
- Abhishek Bhowmick
- Nicolas Loizou
- Simon Lacoste-Julien
- John C. Duchi
- Vivak Patel
- Julien Freudiger
- Sharan Vaswani
- Prabhu Teja Sivaprasad
- Gaurav Kapoor
Papers from Hilal Asi's perspective
Ordered by how strongly the researchers Hilal Asi trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- The importance of better models in stochastic optimization (2019) — 83 citations
- Stochastic (Approximate) Proximal Point Methods: Convergence, Optimality, and Adaptivity (2018) — 148 citations
- Element Level Differential Privacy: The Right Granularity of Privacy (2019) — 19 citations