Nicole Meister

2023

A researcher in the Science corpus: 1 paper; trusts 0 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Whom Nicole Meister trusts overall

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

1. Rowan Zellers
2. Inbal Magar
3. Yonatan Bisk
4. Geoffrey E. Hinton
5. George Casella
6. Nikhil Kandpal
7. Stephanie C. Lin
8. Diederik P. Kingma
9. Yoshua Bengio

Papers from Nicole Meister's perspective

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

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

Their papers

• Proving Test Set Contamination in Black Box Language Models (2023) — 251 citations