Chloé Braud

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 5 papers; trusts 90 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 81 citations into it
• Natural Language Processing — 51 citations into it
• Computer Vision and Pattern Recognition — 37 citations into it
• Model Architectures — 14 citations into it
• Representation & Self-Supervised Learning — 10 citations into it
• Evaluation & Benchmarks — 7 citations into it

Whom Chloé Braud trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. R. Caruana
2. Alex Graves
3. Diederik P. Kingma
4. J. Urgen Schmidhuber
5. Sepp Hochreiter
6. Geoffrey E. Hinton
7. Yoshua Bengio
8. Ronan Collobert
9. Jason Aaron Edward Weston
10. Barbara Plank

Who trusts Chloé Braud overall

1. Sebastian Ruder

Papers from Chloé Braud's perspective

Ordered by how strongly the researchers Chloé Braud trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Augmenting Transformers with KNN-Based Composite Memory for Dialog (2020) — 86 citations
• Using Local Knowledge Graph Construction to Scale Seq2Seq Models to Multi-Document Inputs (2019) — 109 citations
• When does deep multi-task learning work for loosely related document classification tasks? (2018) — 7 citations
• Cross-lingual and cross-domain discourse segmentation of entire documents (2017) — 36 citations
• Augmenting Transformers with KNN-Based Composite Memory (2019)