Patrick Littell

Artificial Intelligence · 2016–2020

A researcher in the Science corpus: 7 papers; trusts 50 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 70 citations into it; #12,589 of 35,345 by trust here, #9,369 by citations
• Natural Language Processing — 62 citations into it; #3,472 of 5,688 by trust here, #2,955 by citations
• Computer Vision and Pattern Recognition — 21 citations into it
• Evaluation & Benchmarks — 10 citations into it
• Model Architectures — 10 citations into it
• Large Language Models — 7 citations into it
• Optimization & Training — 5 citations into it

Whom Patrick Littell trusts overall

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

1. Kenneth Heafield
2. Diederik P. Kingma
3. Philipp Koehn
4. Andreas Stolcke
5. Taku Kudo
6. Chi-kiu (羅致翹) Lo
7. Yoshua Bengio
8. Geoffrey E. Hinton
9. Alex Graves
10. Jimmy Ba

Who trusts Patrick Littell overall

1. Antonios Anastasopoulos
2. Graham Neubig
3. Philipp Koehn
4. Ahmet Üstün
5. Edoardo Maria Ponti
6. Chaitanya Malaviya

Papers from Patrick Littell's perspective

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

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

Their papers

• Polyglot Neural Language Models: A Case Study in Cross-Lingual Phonetic Representation Learning (2016) — 67 citations
• Learning Language Representations for Typology Prediction (2017) — 112 citations
• URIEL and lang2vec: Representing languages as typological, geographical, and phylogenetic vectors (2017) — 304 citations
• Choosing Transfer Languages for Cross-Lingual Learning (2019) — 265 citations
• Multi-Source Transformer for Kazakh-Russian-English Neural Machine Translation (2019) — 17 citations
• Measuring sentence parallelism using Mahalanobis distances: The NRC unsupervised submissions to the WMT18 Parallel Corpus Filtering shared task (2018) — 8 citations
• Universal Phone Recognition with a Multilingual Allophone System (2020) — 172 citations