Franck Dernoncourt

Artificial Intelligence · 2016–2023

A researcher in the Science corpus: 14 papers; trusts 283 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 407 citations into it; #8,177 of 35,345 by trust here, #5,720 by citations
• Natural Language Processing — 283 citations into it; #3,890 of 5,688 by trust here, #2,333 by citations
• Safety, Robustness & Interpretability — 115 citations into it
• Computer Vision and Pattern Recognition — 83 citations into it; #19,377 of 21,575 by trust here, #15,162 by citations
• Evaluation & Benchmarks — 61 citations into it
• Large Language Models — 54 citations into it
• Representation & Self-Supervised Learning — 32 citations into it
• Molecular Biology — 31 citations into it

Whom Franck Dernoncourt trusts overall

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

1. Tomas Mikolov
2. Ilya Sutskever
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Diederik P. Kingma
6. Yoon Kim
7. Sepp Hochreiter
8. Richard Socher
9. Alex Krizhevsky
10. Alex Graves

Who trusts Franck Dernoncourt overall

1. Yuan Luo
2. Sebastian Gehrmann
3. Manaal Faruqui
4. Weiwei Hu
5. Jianfeng Gao
6. Joel Jang
7. Arman Cohan
8. Rabee Sohail Malik
9. Wojciech Kryscinski
10. Chih-Wen Goo

Papers from Franck Dernoncourt's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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

• A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents (2018) — 950 citations
• Multilingual Twitter Corpus and Baselines for Evaluating Demographic Bias in Hate Speech Recognition (2020) — 105 citations
• Sequential Short-Text Classification with Recurrent and Convolutional Neural Networks (2016) — 469 citations
• Understanding Points of Correspondence between Sentences for Abstractive Summarization (2020) — 29 citations
• De-identification of patient notes with recurrent neural networks (2016) — 433 citations
• Comparing Rule-Based and Deep Learning Models for Patient Phenotyping (2017) — 45 citations
• Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives (2018) — 199 citations
• Bias and Fairness in Large Language Models: A Survey (2023) — 1,188 citations
• ChatGPT Beyond English: Towards a Comprehensive Evaluation of Large Language Models in Multilingual Learning (2023) — 415 citations
• Factual Error Correction for Abstractive Summaries Using Entity Retrieval (2022) — 15 citations
• SemEval-2020 Task 6: Definition Extraction from Free Text with the DEFT Corpus (2020) — 31 citations
• Improving Human Text Comprehension through Semi-Markov CRF-based Neural Section Title Generation (2019) — 10 citations
• TutorialVQA: Question Answering Dataset for Tutorial Videos (2019) — 36 citations
• DEFT: A corpus for definition extraction in free- and semi-structured text (2019) — 45 citations