Salem Alelyani
Artificial Intelligence · 2014–2020
A researcher in the Science corpus: 3 papers; trusts 45 researchers and is trusted by 7 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 47 citations into it; #19,862 of 35,345 by trust here, #21,316 by citations
- Computer Vision and Pattern Recognition — 20 citations into it; #13,451 of 21,575 by trust here, #15,529 by citations
- Statistics and Probability — 17 citations into it
- Information Systems — 13 citations into it
- Molecular Biology — 12 citations into it
- Signal Processing — 9 citations into it
- Computational Theory and Mathematics — 7 citations into it
- Statistical Learning Theory — 7 citations into it
Whom Salem Alelyani trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Robert Tibshirani
- Huan Liu
- Lei Yu
- Ulrike von Luxburg
- Mark A. Hall
- Sanmay Das
- Zheng Zhao
- Manoranjan Dash
- J. Ross Quinlan
- Deng Cai
Who trusts Salem Alelyani overall
- Saúl Solorio-Fernández
- Alan Jović
- Jundong Li
- Vipin Kumar
- J. A. Carrasco-Ochoa
- Karla Brkic
- Tansel Dökeroğlu
- Jiliang Tang
- Yun Li
- José Fco. Martínez-Trinidad
Papers from Salem Alelyani's perspective
Ordered by how strongly the researchers Salem Alelyani 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
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- 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
- Feature Selection for Clustering: A Review (2018) — 423 citations
- Predicting academic performance of students from VLE big data using deep learning models (2020) — 476 citations
- Feature Selection for Classification: A Review (2014) — 1,235 citations