Ronak Pradeep
Artificial Intelligence · 2020–2025
A researcher in the Science corpus: 7 papers; trusts 50 researchers and is trusted by 14 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 112 citations into it; #11,675 of 35,345 by trust here, #7,241 by citations
- Information Retrieval & Search — 62 citations into it; #329 of 1,022 by trust here, #208 by citations
- Information Systems — 41 citations into it
- Computer Vision and Pattern Recognition — 36 citations into it
- Evaluation & Benchmarks — 31 citations into it; #2,616 of 3,147 by trust here, #2,799 by citations
- Large Language Models — 22 citations into it
- Management Science and Operations Research — 6 citations into it
Whom Ronak Pradeep trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Jimmy J. Lin
- Rodrigo Nogueira
- Peilin Yang
- Wei Yang
- Kyunghyun Cho
- Geoffrey E. Hinton
- Yoshua Bengio
- David R. Cheriton
- Diederik P. Kingma
- Ilya Sutskever
Who trusts Ronak Pradeep overall
- Jimmy J. Lin
- Wei-Chao Zhou
- Xinyu Crystina Zhang
- Xueguang Ma
- Gautier Izacard
- Sheng-Chieh Lin
- Jiaxin Zhang
- Jheng-Hong Yang
- Zhuyun Dai
- Ori Ram
Papers from Ronak Pradeep's perspective
Ordered by how strongly the researchers Ronak Pradeep 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)
- 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)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
Their papers
- Document Ranking with a Pretrained Sequence-to-Sequence Model (2020) — 821 citations
- Pyserini: A Python Toolkit for Reproducible Information Retrieval Research with Sparse and Dense Representations (2021) — 673 citations
- A Replication Study of Dense Passage Retriever (2021) — 74 citations
- A benchmark of expert-level academic questions to assess AI capabilities (2025) — 349 citations
- RankZephyr: Effective and Robust Zero-Shot Listwise Reranking is a Breeze! (2023)
- Pyserini: An Easy-to-Use Python Toolkit to Support Replicable IR Research with Sparse and Dense Representations (2021) — 105 citations
- Covidex: Neural Ranking Models and Keyword Search Infrastructure for the COVID-19 Open Research Dataset (2020) — 64 citations