Anna Rumshisky

Artificial Intelligence · 2013–2022

A researcher in the Science corpus: 11 papers; trusts 594 researchers and is trusted by 43 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 366 citations into it; #5,377 of 35,345 by trust here, #2,759 by citations
• Natural Language Processing — 235 citations into it
• Computer Vision and Pattern Recognition — 137 citations into it; #8,193 of 21,575 by trust here, #4,137 by citations
• Large Language Models — 96 citations into it; #161 of 799 by trust here, #309 by citations
• Evaluation & Benchmarks — 73 citations into it; #2,593 of 3,147 by trust here, #2,393 by citations
• Efficiency & Compression — 50 citations into it; #784 of 1,264 by trust here, #968 by citations
• Model Architectures — 50 citations into it
• Safety, Robustness & Interpretability — 49 citations into it

Whom Anna Rumshisky trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Ilya Sutskever
5. Alex Krizhevsky
6. Yann LeCun
7. Kaiming He
8. Alex Graves
9. Sepp Hochreiter
10. Oriol Vinyals

Who trusts Anna Rumshisky overall

1. Anna Rogers
2. Olga Kovaleva
3. Yonatan Belinkov
4. Piyawat Lertvittayakumjorn
5. Peter J. Liu
6. Yuqi Si
7. Di Jin
8. Giovanni Puccetti
9. Gabriel Ilharco
10. Koustuv Sinha

Papers from Anna Rumshisky's perspective

Ordered by how strongly the researchers Anna Rumshisky 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• A Primer in BERTology: What We Know About How BERT Works (2020) — 1,930 citations
• Revealing the Dark Secrets of BERT (2019) — 635 citations
• AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model (2022) — 91 citations
• Getting Closer to AI Complete Question Answering: A Set of Prerequisite Real Tasks (2020) — 141 citations
• What’s in a Name? Reducing Bias in Bios without Access to Protected Attributes (2019) — 86 citations
• When BERT Plays the Lottery, All Tickets Are Winning (2020) — 206 citations
• Adversarial Decomposition of Text Representation (2018) — 47 citations
• BERT Busters: Outlier Dimensions that Disrupt Transformers (2021) — 126 citations
• Evaluating temporal relations in clinical text: 2012 i2b2 Challenge (2013) — 509 citations
• Adversarial Text Generation Without Reinforcement Learning (2018) — 29 citations
• Unfolding Physiological State: Mortality Modelling in Intensive Care Units (2014) — 233 citations