Eleni Triantafillou

Artificial Intelligence · 2017–2023

A researcher in the Science corpus: 5 papers; trusts 391 researchers and is trusted by 94 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 105 citations into it; #4,039 of 35,345 by trust here, #3,289 by citations
• Computer Vision and Pattern Recognition — 84 citations into it; #3,944 of 21,575 by trust here, #3,089 by citations
• Representation & Self-Supervised Learning — 60 citations into it; #576 of 3,145 by trust here, #622 by citations
• Meta & Continual Learning — 17 citations into it
• Optimization & Training — 12 citations into it
• Evaluation & Benchmarks — 10 citations into it
• Safety, Robustness & Interpretability — 8 citations into it
• Model Architectures — 7 citations into it

Whom Eleni Triantafillou trusts overall

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

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

Who trusts Eleni Triantafillou overall

1. Han-Jia Ye
2. De-chuan Zhan
3. Wei-Lun Chao
4. Hexiang Hu
5. Chengwei Qin
6. Steinar Laenen
7. Wei-Hong Li
8. Karl Ridgeway
9. Wenbin Li
10. Luca Bertinetto

Papers from Eleni Triantafillou's perspective

Ordered by how strongly the researchers Eleni Triantafillou 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. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Meta-Learning for Semi-Supervised Few-Shot Classification (2018) — 1,444 citations
• Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples (2019) — 701 citations
• Few-Shot Learning Through an Information Retrieval Lens (2017) — 252 citations
• Towards Unbounded Machine Unlearning (2023) — 324 citations
• Learning a Universal Template for Few-shot Dataset Generalization (2021) — 117 citations