Dorottya Demszky

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 3 papers; trusts 63 researchers and is trusted by 27 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 60 citations into it; #8,446 of 35,345 by trust here, #12,580 by citations
• Natural Language Processing — 47 citations into it; #1,776 of 5,688 by trust here, #2,695 by citations
• Evaluation & Benchmarks — 16 citations into it; #919 of 3,147 by trust here, #1,706 by citations
• Computer Vision and Pattern Recognition — 13 citations into it; #6,278 of 21,575 by trust here, #11,498 by citations
• Model Architectures — 7 citations into it

Whom Dorottya Demszky trusts overall

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

1. Diederik P. Kingma
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Sepp Hochreiter
5. Jimmy Ba
6. Alex Graves
7. Jacob Devlin
8. Ilya Sutskever
9. Dzmitry Bahdanau
10. J. Urgen Schmidhuber

Who trusts Dorottya Demszky overall

1. Harsh Jhamtani
2. Wenpeng Yin
3. Esin Durmus
4. Dragomir R. Radev
5. Marco Tulio Ribeiro
6. Clara Vania
7. Marco Valentino
8. Jifan Chen
9. Daniel Deutsch
10. Shikhar Murty

Papers from Dorottya Demszky's perspective

Ordered by how strongly the researchers Dorottya Demszky trusts endorse each paper. Same corpus, a different order for every researcher.

1. Attention is All you Need (Neural Information Processing Systems)
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Long Short-Term Memory
5. ImageNet classification with deep convolutional neural networks
6. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
7. Gradient-based learning applied to document recognition
8. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
9. ImageNet: A large-scale hierarchical image database
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Transforming Question Answering Datasets Into Natural Language Inference Datasets (2018) — 187 citations
• GoEmotions: A Dataset of Fine-Grained Emotions (2020) — 1,171 citations
• Learning to Recognize Dialect Features (2020) — 43 citations