Seraphina Goldfarb-Tarrant

Artificial Intelligence · 2020–2023

A researcher in the Science corpus: 3 papers; trusts 38 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 75 citations into it; #35,147 of 35,345 by trust here, #14,438 by citations
• Natural Language Processing — 60 citations into it
• Safety, Robustness & Interpretability — 49 citations into it
• Representation & Self-Supervised Learning — 9 citations into it
• Safety Research — 7 citations into it

Whom Seraphina Goldfarb-Tarrant trusts overall

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

1. Michael Lewis
2. Kenton C. T. Lee
3. Tomas Mikolov
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Angela Fan
7. Diederik P. Kingma
8. Ilya Sutskever
9. Alex Krizhevsky
10. Jieyu Zhao

Who trusts Seraphina Goldfarb-Tarrant overall

1. Tuhin Chakrabarty
2. Piotr Wojciech Mirowski
3. Gavin Abercrombie
4. Denis Emelin
5. Nikil Selvam
6. Kory Wallace Mathewson
7. Xurui Zhang
8. Oskar van der Wal
9. Ronan Le Bras
10. Amanda Cercas Curry

Papers from Seraphina Goldfarb-Tarrant's perspective

Ordered by how strongly the researchers Seraphina Goldfarb-Tarrant 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
7. ImageNet: A large-scale hierarchical image database
8. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
9. Gradient-based learning applied to document recognition
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Intrinsic Bias Metrics Do Not Correlate with Application Bias (2020) — 214 citations
• Content Planning for Neural Story Generation with Aristotelian Rescoring (2020) — 141 citations
• This Prompt is Measuring : Evaluating Bias Evaluation in Language Models (2023) — 17 citations