Shikha Bordia

Artificial Intelligence · 2019

A researcher in the Science corpus: 4 papers; trusts 102 researchers and is trusted by 53 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 75 citations into it; #4,419 of 35,345 by trust here, #3,726 by citations
• Natural Language Processing — 73 citations into it; #2,222 of 5,688 by trust here, #1,235 by citations
• Representation & Self-Supervised Learning — 24 citations into it
• Computer Vision and Pattern Recognition — 18 citations into it
• Safety, Robustness & Interpretability — 18 citations into it; #354 of 2,251 by trust here, #359 by citations
• Evaluation & Benchmarks — 15 citations into it
• Large Language Models — 9 citations into it
• AI Ethics, Fairness & Society — 7 citations into it

Whom Shikha Bordia trusts overall

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

1. Hila Gonen
2. Tomas Mikolov
3. Stephen Merity
4. Jieyu Zhao
5. Yoav Goldberg
6. Richard Socher
7. Yoshua Bengio
8. Geoffrey E. Hinton
9. Ilya Sutskever
10. Diederik P. Kingma

Who trusts Shikha Bordia overall

1. Haochen Liu
2. Emily Sheng
3. Eric Michael Smith
4. Karolina Stańczak
5. Soumya Barikeri
6. Dana Pessach
7. Kai-Wei Chang
8. Jiaxu Zhao
9. Percy Liang
10. Andrew Gaut

Papers from Shikha Bordia's perspective

Ordered by how strongly the researchers Shikha Bordia 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

• On Measuring Social Biases in Sentence Encoders (2019) — 730 citations
• Identifying and Reducing Gender Bias in Word-Level Language Models (2019) — 372 citations
• Investigating BERT’s Knowledge of Language: Five Analysis Methods with NPIs (2019) — 133 citations
• Do Attention Heads in BERT Track Syntactic Dependencies? (2019) — 151 citations