Julia Gong

Artificial Intelligence · 2021

A researcher in the Science corpus: 1 paper; trusts 3 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 16 citations into it; #29,230 of 35,345 by trust here, #31,361 by citations
• Natural Language Processing — 15 citations into it; #4,745 of 5,688 by trust here, #5,296 by citations
• Evaluation & Benchmarks — 5 citations into it; #2,509 of 3,147 by trust here, #3,004 by citations

Whom Julia Gong trusts overall

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

1. Tal Linzen
2. Rebecca Marvin
3. Jacob Devlin

Who trusts Julia Gong overall

1. Sebastian Schuster

Papers from Julia Gong's perspective

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

1. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
2. Language Models are Unsupervised Multitask Learners
3. Attention is All you Need (Neural Information Processing Systems)
4. Long Short-Term Memory
5. ImageNet classification with deep convolutional neural networks
6. RoBERTa: A Robustly Optimized BERT Pretraining Approach (arXiv.org)
7. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
8. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Building a Large Annotated Corpus of English: The Penn Treebank

Their papers

• Refining Targeted Syntactic Evaluation of Language Models (2021) — 52 citations