Alexandros Potamianos

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 3 papers; trusts 168 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 87 citations into it; #16,612 of 35,345 by trust here, #16,166 by citations
• Natural Language Processing — 60 citations into it; #2,944 of 5,688 by trust here, #3,421 by citations
• Computer Vision and Pattern Recognition — 25 citations into it
• Representation & Self-Supervised Learning — 22 citations into it
• Model Architectures — 14 citations into it
• Optimization & Training — 6 citations into it
• Evaluation & Benchmarks — 5 citations into it

Whom Alexandros Potamianos trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Ilya Sutskever
4. Diederik P. Kingma
5. Alex Krizhevsky
6. Sepp Hochreiter
7. Jimmy Ba
8. Yann LeCun
9. Oriol Vinyals
10. Dzmitry Bahdanau

Who trusts Alexandros Potamianos overall

1. Timo Schick
2. Sebastian Gehrmann

Papers from Alexandros Potamianos's perspective

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

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

Their papers

• An Embarrassingly Simple Approach for Transfer Learning from Pretrained Language Models (2019) — 143 citations
• SEQˆ3: Differentiable Sequence-to-Sequence-to-Sequence Autoencoder for Unsupervised Abstractive Sentence Compression (2019) — 86 citations
• UDALM: Unsupervised Domain Adaptation through Language Modeling (2021) — 64 citations