Christine Basta

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 4 papers; trusts 73 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 65 citations into it; #20,040 of 35,345 by trust here, #10,942 by citations
• Natural Language Processing — 64 citations into it; #3,275 of 5,688 by trust here, #2,333 by citations
• Safety, Robustness & Interpretability — 25 citations into it; #1,250 of 2,251 by trust here, #939 by citations
• Computer Vision and Pattern Recognition — 20 citations into it
• Representation & Self-Supervised Learning — 12 citations into it
• Large Language Models — 7 citations into it
• Machine Translation — 6 citations into it

Whom Christine Basta trusts overall

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

1. Matthew E. Peters
2. Jieyu Zhao
3. Leland McInnes
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Ilya Sutskever
7. Marcin Junczys-Dowmunt
8. Diederik P. Kingma
9. Ashish Vaswani
10. Tomas Mikolov

Who trusts Christine Basta overall

1. Marta Ruiz Costa-jussà
2. Rishabh Bhardwaj
3. Emily Sheng
4. Pieter Delobelle
5. Andrew Silva
6. Adrià de Jorge
7. Soujanya Poria
8. Navonil Majumder
9. Kai-Wei Chang
10. Keita Kurita

Papers from Christine Basta's perspective

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

Their papers

• Evaluating the Underlying Gender Bias in Contextualized Word Embeddings (2019) — 226 citations
• The TALP-UPC Machine Translation Systems for WMT19 News Translation Task: Pivoting Techniques for Low Resource MT (2019) — 16 citations
• Towards Mitigating Gender Bias in a decoder-based Neural Machine Translation model by Adding Contextual Information (2020) — 33 citations
• Gender Bias in Multilingual Neural Machine Translation: The Architecture Matters (2020) — 20 citations