Damian Pascual

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 5 papers; trusts 106 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 113 citations into it; #33,004 of 35,345 by trust here, #15,542 by citations
• Natural Language Processing — 69 citations into it
• Computer Vision and Pattern Recognition — 51 citations into it
• Large Language Models — 40 citations into it
• Model Architectures — 31 citations into it; #2,098 of 2,278 by trust here, #1,490 by citations
• Safety, Robustness & Interpretability — 14 citations into it
• Evaluation & Benchmarks — 10 citations into it
• Representation & Self-Supervised Learning — 10 citations into it

Whom Damian Pascual trusts overall

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

1. Alec Radford
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Diederik P. Kingma
5. Ilya Sutskever
6. Ashish Vaswani
7. Alex Graves
8. Jacob Devlin
9. Alex Krizhevsky
10. Jimmy Ba

Who trusts Damian Pascual overall

1. Sachin Kumar
2. Hanqing Zhang
3. Derya Soydaner
4. Biswajit Paria
5. Yu Meng
6. Sarah Wiegreffe
7. Badr AlKhamissi

Papers from Damian Pascual's perspective

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

Their papers

• On Identifiability in Transformers (2019) — 212 citations
• Of Non-Linearity and Commutativity in BERT (2021) — 20 citations
• EEGEyeNet: a Simultaneous Electroencephalography and Eye-tracking Dataset and Benchmark for Eye Movement Prediction (2021)
• A Plug-and-Play Method for Controlled Text Generation (2021) — 109 citations
• Telling BERT’s Full Story: from Local Attention to Global Aggregation (2020) — 22 citations