Paweł Budzianowski

Artificial Intelligence · 2017–2019

A researcher in the Science corpus: 8 papers; trusts 154 researchers and is trusted by 84 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 177 citations into it; #3,045 of 35,345 by trust here, #4,041 by citations
• Natural Language Processing — 139 citations into it; #870 of 5,688 by trust here, #1,059 by citations
• Reinforcement Learning — 59 citations into it
• Computer Vision and Pattern Recognition — 24 citations into it
• Dialogue & Conversational AI — 17 citations into it
• Evaluation & Benchmarks — 13 citations into it; #1,534 of 3,147 by trust here, #928 by citations
• Model Architectures — 9 citations into it
• Large Language Models — 8 citations into it

Whom Paweł Budzianowski trusts overall

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

1. Yoshua Bengio
2. Ilya Sutskever
3. Matthew Henderson
4. Geoffrey E. Hinton
5. Diederik P. Kingma
6. Milica Gasic
7. Richard S. Sutton
8. Oriol Vinyals
9. Susannah Young
10. Alex Krizhevsky

Who trusts Paweł Budzianowski overall

1. Zhaojiang Lin
2. Fei Mi
3. Mihir Kale
4. Andrea Madotto
5. Christian Geishauser
6. Baolin Peng
7. Minlie Huang
8. Ehsan Hosseini-Asl
9. Yichi Zhang
10. Abhinav Rastogi

Papers from Paweł Budzianowski's perspective

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

1. ImageNet classification with deep convolutional neural networks
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. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
8. ImageNet: A large-scale hierarchical image database
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

• MultiWOZ - A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling (2018) — 1,527 citations
• Hello, It’s GPT-2 - How Can I Help You? Towards the Use of Pretrained Language Models for Task-Oriented Dialogue Systems (2019) — 340 citations
• Sample-efficient Actor-Critic Reinforcement Learning with Supervised Data for Dialogue Management (2017) — 135 citations
• A Repository of Conversational Datasets (2019) — 97 citations
• Towards end-to-end multi-domain dialogue modelling (2018) — 18 citations
• Feudal Reinforcement Learning for Dialogue Management in Large Domains (2018) — 50 citations
• Sample Efficient Deep Reinforcement Learning for Dialogue Systems With Large Action Spaces (2018) — 87 citations
• Variational Cross-domain Natural Language Generation for Spoken Dialogue Systems (2018) — 14 citations