Stephanie L. Hyland

Artificial Intelligence · 2017–2023

A researcher in the Science corpus: 6 papers; trusts 175 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 168 citations into it; #10,445 of 35,345 by trust here, #12,083 by citations
• Computer Vision and Pattern Recognition — 103 citations into it; #7,153 of 21,575 by trust here, #8,612 by citations
• Optimization & Training — 33 citations into it
• Representation & Self-Supervised Learning — 31 citations into it
• Safety, Robustness & Interpretability — 25 citations into it
• Privacy & Security — 21 citations into it
• Generative Models — 20 citations into it; #685 of 1,956 by trust here, #1,054 by citations
• Natural Language Processing — 20 citations into it

Whom Stephanie L. Hyland trusts overall

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

1. Alex Krizhevsky
2. Diederik P. Kingma
3. L. Eon Bottou
4. Geoffrey E. Hinton
5. Cynthia Dwork
6. Yoshua Bengio
7. Kamalika Chaudhuri
8. Yann LeCun
9. Martín Abadi
10. Moritz Hardt

Who trusts Stephanie L. Hyland overall

1. Eoin Brophy
2. Jinsung Yoon
3. Zhengwei Wang
4. Suman V. Ravuri
5. Anvita Gupta
6. James Jordon
7. Anne Marie Delaney
8. Laura Aviñó
9. Tomás Ward
10. Alexandre Yahi

Papers from Stephanie L. Hyland's perspective

Ordered by how strongly the researchers Stephanie L. Hyland 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. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Making the Most of Text Semantics to Improve Biomedical Vision-Language Processing (2022) — 489 citations
• Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs (2017) — 984 citations
• Learning to Exploit Temporal Structure for Biomedical Vision-Language Processing (2023) — 304 citations
• AN EMPIRICAL STUDY (2022)
• Early prediction of circulatory failure in the intensive care unit using machine learning (2020) — 430 citations
• On the Intrinsic Privacy of Stochastic Gradient Descent (2019) — 13 citations