Pat Verga

Artificial Intelligence · 2016–2024

A researcher in the Science corpus: 10 papers; trusts 498 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 250 citations into it; #14,773 of 35,345 by trust here, #7,310 by citations
• Natural Language Processing — 173 citations into it; #3,482 of 5,688 by trust here, #2,846 by citations
• Computer Vision and Pattern Recognition — 85 citations into it
• Model Architectures — 45 citations into it
• Evaluation & Benchmarks — 33 citations into it
• Representation & Self-Supervised Learning — 32 citations into it
• Information Retrieval & Search — 28 citations into it
• Molecular Biology — 27 citations into it

Whom Pat Verga trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Diederik P. Kingma
4. Sepp Hochreiter
5. Alex Krizhevsky
6. Ilya Sutskever
7. Yann LeCun
8. Kaiming He
9. Alex Graves
10. Richard Socher

Who trusts Pat Verga overall

1. Badr AlKhamissi
2. Tom Young
3. Emma Strubell
4. Haitian Sun
5. Chen Zhu
6. Yoon Kim
7. Millicent Li
8. Nitish Gupta
9. Yanpeng Zhao
10. Oshin Agarwal

Papers from Pat Verga's perspective

Ordered by how strongly the researchers Pat Verga 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. 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

• Linguistically-Informed Self-Attention for Semantic Role Labeling (2018) — 401 citations
• MuRAG: Multimodal Retrieval-Augmented Generator for Open Question Answering over Images and Text (2022) — 304 citations
• Facts as Experts: Adaptable and Interpretable Neural Memory over Symbolic Knowledge (2020) — 54 citations
• Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Auto-Encoders (2019) — 129 citations
• Fast and Accurate Entity Recognition with Iterated Dilated Convolutions (2017) — 430 citations
• Generalizing to Unseen Entities and Entity Pairs with Row-less Universal Schema (2016) — 48 citations
• Reasoning Over Virtual Knowledge Bases With Open Predicate Relations (2021) — 27 citations
• Marginal Likelihood Training of BiLSTM-CRF for Biomedical Named Entity Recognition from Disjoint Label Sets (2018) — 67 citations
• Replacing Judges with Juries: Evaluating LLM Generations with a Panel of Diverse Models (2024) — 271 citations
• Fast and Accurate Sequence Labeling with Iterated Dilated Convolutions (2017) — 42 citations