Arzoo Katiyar

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 2 papers; trusts 105 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 57 citations into it; #10,574 of 35,345 by trust here, #25,809 by citations
• Natural Language Processing — 47 citations into it
• Computer Vision and Pattern Recognition — 16 citations into it
• Evaluation & Benchmarks — 11 citations into it
• Model Architectures — 10 citations into it
• Representation & Self-Supervised Learning — 10 citations into it
• Optimization & Training — 7 citations into it

Whom Arzoo Katiyar trusts overall

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

1. Ilya Sutskever
2. Geoffrey E. Hinton
3. Matthew D. Zeiler
4. Oriol Vinyals
5. Yoshua Bengio
6. Sepp Hochreiter
7. Makoto Miwa
8. Alex Krizhevsky
9. Diederik P. Kingma
10. Richard Socher

Who trusts Arzoo Katiyar overall

1. Zexuan Zhong
2. Victor Sanh
3. Yu-Siang Wang
4. Giannis Bekoulis
5. Ying Lin
6. Danqi Chen
7. Xiaoya Li
8. Sebastian Ruder
9. Thomas Wolf
10. Heng Ji

Papers from Arzoo Katiyar's perspective

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

Their papers

• Revisiting Few-sample BERT Fine-tuning (2020) — 505 citations
• Going out on a limb: Joint Extraction of Entity Mentions and Relations without Dependency Trees (2017) — 244 citations