Pinkesh Badjatiya

Artificial Intelligence · 2017–2019

A researcher in the Science corpus: 2 papers; trusts 14 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 22 citations into it; #9,793 of 35,345 by trust here, #9,968 by citations
• Natural Language Processing — 14 citations into it; #4,790 of 5,688 by trust here, #5,214 by citations
• Safety, Robustness & Interpretability — 14 citations into it; #1,850 of 2,251 by trust here, #2,161 by citations
• AI Ethics, Fairness & Society — 7 citations into it
• Information Systems — 6 citations into it
• Safety Research — 5 citations into it

Whom Pinkesh Badjatiya trusts overall

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

1. Jeffrey Pennington
2. George A. Miller
3. Yoon Kim
4. Zeerak Talat
5. Richard Socher
6. Lucas Dixon
7. Dirk Hovy
8. Aylin Caliskan
9. Ellery Wulczyn
10. Christopher D. Manning

Who trusts Pinkesh Badjatiya overall

1. Anthony Rios
2. Pushkar Mishra
3. John Pavlopoulos
4. Antigoni-Maria Founta
5. Michael Wiegand
6. Ji Ho Park
7. Endang Wahyu Pamungkas
8. Mladen Karan
9. Kanika Narang
10. Paula Fortuna

Papers from Pinkesh Badjatiya's perspective

Ordered by how strongly the researchers Pinkesh Badjatiya 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. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Stereotypical Bias Removal for Hate Speech Detection Task using Knowledge-based Generalizations (2019) — 111 citations
• Deep Learning for Hate Speech Detection in Tweets (2017) — 1,283 citations