Andrew Drozdov

Artificial Intelligence · 2016–2025

A researcher in the Science corpus: 6 papers; trusts 113 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 118 citations into it; #13,510 of 35,345 by trust here, #11,490 by citations
• Natural Language Processing — 71 citations into it; #2,819 of 5,688 by trust here, #2,888 by citations
• Computer Vision and Pattern Recognition — 47 citations into it
• Evaluation & Benchmarks — 27 citations into it
• Information Retrieval & Search — 20 citations into it
• Model Architectures — 14 citations into it; #1,144 of 2,278 by trust here, #1,530 by citations
• Representation & Self-Supervised Learning — 13 citations into it
• Reinforcement Learning — 11 citations into it

Whom Andrew Drozdov trusts overall

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

1. Richard Socher
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Christopher D. Manning
5. Yoshua Bengio
6. Ronald J. Williams
7. Alex Krizhevsky
8. Ilya Sutskever
9. Kaiming He
10. Yann LeCun

Who trusts Andrew Drozdov overall

1. Yanpeng Zhao
2. Yoon Kim
3. Jiayuan Mao
4. Ivan Titov
5. Taeuk Kim
6. Freda Shi
7. Alessandro Raganato
8. Wenhu Chen
9. R. Thomas McCoy
10. Adrian Weller

Papers from Andrew Drozdov's perspective

Ordered by how strongly the researchers Andrew Drozdov 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
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

• Do latent tree learning models identify meaningful structure in sentences? (2017) — 123 citations
• Emergent Language in a Multi-Modal, Multi-Step Referential Game (2017) — 31 citations
• Compositional Semantic Parsing with Large Language Models (2022) — 113 citations
• Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Auto-Encoders (2019) — 129 citations
• FreshStack: Building Realistic Benchmarks for Evaluating Retrieval on Technical Documents (2025)
• Understanding Mutual Information and its Use in InfoGAN (2016)