Arkil Patel
Artificial Intelligence · 2021–2025
A researcher in the Science corpus: 2 papers; trusts 27 researchers and is trusted by 30 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 51 citations into it; #8,207 of 35,345 by trust here, #8,439 by citations
- Natural Language Processing — 33 citations into it; #1,719 of 5,688 by trust here, #2,082 by citations
- Evaluation & Benchmarks — 25 citations into it
- Computer Vision and Pattern Recognition — 14 citations into it
- Information Systems — 12 citations into it; #1,273 of 8,771 by trust here, #1,265 by citations
Whom Arkil Patel trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Tal Linzen
- Yonatan Belinkov
- Zhipeng Xie
- Sarah Wiegreffe
- Yan Wang
- Thang Luong
- Geoffrey E. Hinton
- Yoshua Bengio
- Shen-Yun Miao
- Diederik P. Kingma
Who trusts Arkil Patel overall
- Wenhu Chen
- Pan Lu
- Yifei Li
- Lei Wang
- Yihuai Lan
- Geunwoo Kim
- Namgyu Ho
- Zhanming Jie
- Shima Imani
- Chadi Helwe
Papers from Arkil Patel's perspective
Ordered by how strongly the researchers Arkil Patel trusts endorse each paper. Same corpus, a different order for every researcher.
- Attention is All you Need (Neural Information Processing Systems)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- ImageNet: A large-scale hierarchical image database
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
Their papers
- Are NLP Models really able to Solve Simple Math Word Problems? (2021) — 1,284 citations
- A benchmark of expert-level academic questions to assess AI capabilities (2025) — 349 citations