Fabio A. González

Computer Vision and Pattern Recognition · Medical Image Anal. · 2013–2017

A researcher in the Science corpus: 7 papers; trusts 171 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 83 citations into it; #14,137 of 21,575 by trust here, #4,781 by citations
• Artificial Intelligence — 72 citations into it; #22,653 of 35,345 by trust here, #4,829 by citations
• Representation & Self-Supervised Learning — 26 citations into it
• Multimodal Learning — 13 citations into it; #1,056 of 1,211 by trust here, #962 by citations
• Model Architectures — 12 citations into it
• Natural Language Processing — 9 citations into it
• Signal Processing — 9 citations into it
• Optimization & Training — 8 citations into it

Whom Fabio A. González trusts overall

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

1. Yoshua Bengio
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Yann LeCun
5. Diederik P. Kingma
6. Ilya Sutskever
7. Ruslan Salakhutdinov
8. Richard Socher
9. Andrew Y. Ng
10. Karen Simonyan

Who trusts Fabio A. González overall

1. S. Suganyadevi
2. Andrew Janowczyk
3. Mufti Mahmud

Papers from Fabio A. González's perspective

Ordered by how strongly the researchers Fabio A. González 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Gated Multimodal Units for Information Fusion (2017) — 625 citations
• Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks (2014) — 632 citations
• A Deep Learning Architecture for Image Representation, Visual Interpretability and Automated Basal-Cell Carcinoma Cancer Detection (2013) — 389 citations
• Assessment of algorithms for mitosis detection in breast cancer histopathology images (2014) — 464 citations
• Representation learning for mammography mass lesion classification with convolutional neural networks (2016) — 443 citations
• Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent (2017) — 479 citations
• Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features (2014) — 334 citations