Nidhi Vyas

Artificial Intelligence · 2019

A researcher in the Science corpus: 2 papers; trusts 17 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 36 citations into it; #22,162 of 35,345 by trust here, #8,999 by citations
• Natural Language Processing — 26 citations into it
• Safety, Robustness & Interpretability — 24 citations into it; #1,343 of 2,251 by trust here, #715 by citations
• Representation & Self-Supervised Learning — 18 citations into it
• AI Ethics, Fairness & Society — 8 citations into it
• Safety Research — 6 citations into it

Whom Nidhi Vyas trusts overall

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

1. A. Greenwald
2. Aylin Caliskan
3. Daniel D. Lee
4. Tomas Mikolov
5. Tolga Bolukbasi
6. Matthew E. Peters
7. Jieyu Zhao
8. Jacob Devlin
9. Ilya Sutskever
10. Ian Tenney

Who trusts Nidhi Vyas overall

1. Pieter Delobelle
2. Omar U. Florez
3. Percy Liang
4. Svetlana Kiritchenko
5. Andrew Silva
6. João Sedoc
7. Wei Guo
8. Robert Munro
9. Oskar van der Wal
10. Paula Czarnowska

Papers from Nidhi Vyas's perspective

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

1. Attention is All you Need (Neural Information Processing Systems)
2. Long Short-Term Memory
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. GloVe: Global Vectors for Word Representation
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. ImageNet classification with deep convolutional neural networks
7. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
8. Distributed Representations of Words and Phrases and their Compositionality (Neural Information Processing Systems)
9. ImageNet: A large-scale hierarchical image database
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Measuring Bias in Contextualized Word Representations (2019) — 517 citations
• Quantifying Social Biases in Contextual Word Representations (2019) — 37 citations