Frank Rudzicz

Artificial Intelligence · 2011–2021

A researcher in the Science corpus: 8 papers; trusts 247 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 250 citations into it; #16,362 of 35,345 by trust here, #11,327 by citations
• Natural Language Processing — 152 citations into it; #2,903 of 5,688 by trust here, #2,605 by citations
• Computer Vision and Pattern Recognition — 76 citations into it
• Representation & Self-Supervised Learning — 48 citations into it
• Model Architectures — 32 citations into it
• Cognitive Neuroscience — 26 citations into it
• Evaluation & Benchmarks — 26 citations into it
• Safety, Robustness & Interpretability — 25 citations into it

Whom Frank Rudzicz trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Diederik P. Kingma
4. Alex Krizhevsky
5. Ilya Sutskever
6. Kaiming He
7. Yann LeCun
8. Alex Graves
9. Jimmy Ba
10. Tomas Mikolov

Who trusts Frank Rudzicz overall

1. Fei Li
2. Qian Liu
3. Mark Diaz
4. Kaichun Yao
5. Meishan Zhang
6. Hernisa Kacorri
7. Zhiqiang Gao
8. Yonatan Belinkov
9. Isaac L. Johnson
10. Libo Zhang

Papers from Frank Rudzicz's perspective

Ordered by how strongly the researchers Frank Rudzicz 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. 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

• Sequential Explanations with Mental Model-Based Policies (2020) — 16 citations
• Computational Linguistics: 16th International Conference of the Pacific Association for Computational Linguistics, PACLING 2019, Hanoi, Vietnam, October 11–13, 2019, Revised Selected Papers (2019) — 614 citations
• BENDR: Using Transformers and a Contrastive Self-Supervised Learning Task to Learn From Massive Amounts of EEG Data (2021) — 368 citations
• An Information Theoretic View on Selecting Linguistic Probes (2020) — 22 citations
• Artificial Intelligence and the Implementation Challenge (2019) — 320 citations
• On the importance of normative data in speech-based assessment (2017) — 27 citations
• Linguistic Features Identify Alzheimer’s Disease in Narrative Speech (2015) — 775 citations
• The TORGO database of acoustic and articulatory speech from speakers with dysarthria (2011) — 478 citations