Leila Wehbe
Artificial Intelligence · 2014–2019
A researcher in the Science corpus: 4 papers; trusts 66 researchers and is trusted by 6 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 26 citations into it; #17,881 of 35,345 by trust here, #19,439 by citations
- Cognitive Neuroscience — 23 citations into it; #6,491 of 10,852 by trust here, #6,723 by citations
- Natural Language Processing — 23 citations into it
- Representation & Self-Supervised Learning — 14 citations into it
- Statistics and Probability — 13 citations into it; #8,876 of 12,932 by trust here, #10,855 by citations
- Statistical Learning Theory — 11 citations into it; #847 of 1,038 by trust here, #985 by citations
- Developmental and Educational Psychology — 7 citations into it
- Computer Vision and Pattern Recognition — 5 citations into it
Whom Leila Wehbe trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Arthur Gretton
- Francis R. Bach
- Bernhard Schölkopf
- Geoffrey E. Hinton
- Yoav Goldberg
- Kenji Fukumizu
- Yoshua Bengio
- Alex Smola
- Michael I. Jordan
- Brian Murphy
Who trusts Leila Wehbe overall
- Kirthevasan Kandasamy
- Mariya Toneva
- Charlotte Caucheteux
- Kyunghyun Cho
- Afra Alishahi
- Seth R. Flaxman
- Ahmed Elgohary
- Yaoliang Yu
- Yonatan Belinkov
- Jean-Remi King
Papers from Leila Wehbe's perspective
Ordered by how strongly the researchers Leila Wehbe 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)
- Attention is All you Need (Neural Information Processing Systems)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- A Compositional and Interpretable Semantic Space (2015) — 70 citations
- Nonparametric Independence Testing for Small Sample Sizes (2014) — 14 citations
- Interpreting and improving natural-language processing (in machines) with natural language-processing (in the brain) (2019) — 312 citations
- Simultaneously Uncovering the Patterns of Brain Regions Involved in Different Story Reading Subprocesses (2014) — 312 citations