Aurélie Herbelot
Natural Language Processing · 2012–2020
A researcher in the Science corpus: 7 papers; trusts 119 researchers and is trusted by 5 researchers here, as inferred from the citation graph.
Subfields
- Natural Language Processing — 82 citations into it
- Artificial Intelligence — 79 citations into it; #17,134 of 35,345 by trust here, #13,962 by citations
- Computer Vision and Pattern Recognition — 40 citations into it; #10,858 of 21,575 by trust here, #11,950 by citations
- Representation & Self-Supervised Learning — 40 citations into it
- Multimodal Learning — 22 citations into it
- Computational Linguistics & Semantics — 18 citations into it
- Evaluation & Benchmarks — 8 citations into it
- Language and Linguistics — 6 citations into it
Whom Aurélie Herbelot trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Marco Baroni
- Tomas Mikolov
- K. Erk
- Stephen Clark
- Ilya Sutskever
- Yoshua Bengio
- Geoffrey E. Hinton
- Sebastian Padó
- Elia Bruni
- Josh Tenenbaum
Who trusts Aurélie Herbelot overall
- Timo Schick
- Jingyuan Sun
- Hinrich Schütze
- Tom Young
- Shaonan Wang
- Gemma Boleda
- Chengqing Zong
- Jiwei Li
- Tao Lei
- Emily M. Bender
Papers from Aurélie Herbelot's perspective
Ordered by how strongly the researchers Aurélie Herbelot 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)
- Long Short-Term Memory
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
Their papers
- FOIL it! Find One mismatch between Image and Language caption (2017) — 162 citations
- High-risk learning: acquiring new word vectors from tiny data (2017) — 84 citations
- Building a shared world: mapping distributional to model-theoretic semantic spaces (2015) — 73 citations
- How to marry a star: Probabilistic constraints for meaning in context (2020) — 8 citations
- “Look, some Green Circles!”: Learning to Quantify from Images (2016) — 18 citations
- Formal Distributional Semantics: Introduction to the Special Issue (2016) — 45 citations
- Distributional techniques for philosophical enquiry (2012) — 19 citations