Jaime S. Cardoso
Artificial Intelligence · 2007–2020
A researcher in the Science corpus: 8 papers; trusts 109 researchers and is trusted by 14 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 91 citations into it; #12,700 of 35,345 by trust here, #8,525 by citations
- Computer Vision and Pattern Recognition — 51 citations into it
- Safety, Robustness & Interpretability — 44 citations into it
- Radiology, Nuclear Medicine and Imaging — 10 citations into it; #1,315 of 3,265 by trust here, #1,484 by citations
- Natural Language Processing — 9 citations into it
- Oncology — 9 citations into it
- Model Architectures — 8 citations into it
- Optimization & Training — 8 citations into it
Whom Jaime S. Cardoso trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Alex Krizhevsky
- Been Kim
- Kaiming He
- Yoshua Bengio
- Ilya Sutskever
- Yann LeCun
- Diederik P. Kingma
- Matthew D. Zeiler
- Karen Simonyan
Who trusts Jaime S. Cardoso overall
Papers from Jaime S. Cardoso's perspective
Ordered by how strongly the researchers Jaime S. Cardoso trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Going deeper with convolutions
- Long Short-Term Memory
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Automation of Waste Sorting with Deep Learning (2019) — 71 citations
- Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support (2017) — 432 citations
- Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms (2020) — 345 citations
- Machine Learning Interpretability: A Survey on Methods and Metrics (2019) — 1,674 citations
- Towards Complementary Explanations Using Deep Neural Networks (2018) — 50 citations
- Optical music recognition: state-of-the-art and open issues (2012) — 308 citations
- INbreast: toward a full-field digital mammographic database. (2012) — 1,155 citations
- Matched-filtering and parameter estimation of ringdown waveforms (2007) — 153 citations