Gilmer Valdes
Artificial Intelligence · 2016–2021
A researcher in the Science corpus: 5 papers; trusts 15 researchers and is trusted by 1 researcher here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 73 citations into it
- Computer Vision and Pattern Recognition — 54 citations into it
- Statistics and Probability — 11 citations into it
- Information Systems — 7 citations into it
- Statistical Learning Theory — 7 citations into it
- Optimization & Training — 5 citations into it
Whom Gilmer Valdes trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Jerome H. Friedman
- R. Caruana
- Jaka Demsar
- Yoav Freund
- Alex Krizhevsky
- Alexandru Niculescu-Mizil
- Geoffrey E. Hinton
- Robert E. Schapire
- David G. Lowe
- Diederik P. Kingma
Who trusts Gilmer Valdes overall
Papers from Gilmer Valdes's perspective
Ordered by how strongly the researchers Gilmer Valdes trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Gradient-based learning applied to document recognition
- Dropout: a simple way to prevent neural networks from overfitting
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- An unsupervised convolutional neural network-based algorithm for deformable image registration (2018) — 58 citations
- Artificial intelligence and machine learning for medical imaging: A technology review. (2021) — 378 citations
- Machine Learning in Radiation Oncology: Opportunities, Requirements, and Needs (2018) — 102 citations
- A Deep Look Into the Future of Quantitative Imaging in Oncology: A Statement of Working Principles and Proposal for Change. (2018) — 99 citations
- MediBoost: a Patient Stratification Tool for Interpretable Decision Making in the Era of Precision Medicine (2016) — 111 citations