Veronica Latcinnik

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 2 papers; trusts 184 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 70 citations into it; #10,507 of 35,345 by trust here, #15,866 by citations
• Natural Language Processing — 58 citations into it; #2,067 of 5,688 by trust here, #3,282 by citations
• Computer Vision and Pattern Recognition — 31 citations into it; #18,040 of 21,575 by trust here, #14,822 by citations
• Large Language Models — 14 citations into it
• Evaluation & Benchmarks — 12 citations into it
• Model Architectures — 8 citations into it
• Safety, Robustness & Interpretability — 8 citations into it
• Prompting & Reasoning — 5 citations into it

Whom Veronica Latcinnik trusts overall

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

1. Ronald J. Williams
2. Yoshua Bengio
3. Ilya Sutskever
4. Geoffrey E. Hinton
5. Diederik P. Kingma
6. Alex Krizhevsky
7. Yann LeCun
8. Kaiming He
9. Alex Graves
10. Oriol Vinyals

Who trusts Veronica Latcinnik overall

1. Bhargavi Paranjape
2. Jiacheng Liu
3. Faeze Brahman
4. Kaj Bostrom
5. Sarah Wiegreffe
6. Ana Marasovic
7. Alane Suhr
8. Vered Shwartz
9. Yejin Choi
10. Xinyu Zhao

Papers from Veronica Latcinnik's perspective

Ordered by how strongly the researchers Veronica Latcinnik 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. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Weakly Supervised Semantic Parsing with Abstract Examples (2017) — 70 citations
• Explaining Question Answering Models through Text Generation (2020) — 53 citations