José Padarian
Environmental Engineering · 2016–2019
A researcher in the Science corpus: 4 papers; trusts 23 researchers and is trusted by 2 researchers here, as inferred from the citation graph.
Subfields
- Environmental Engineering — 55 citations into it; #3,769 of 5,479 by trust here, #2,775 by citations
- Artificial Intelligence — 46 citations into it; #28,416 of 35,345 by trust here, #23,077 by citations
- Soil Science — 40 citations into it
- Civil and Structural Engineering — 24 citations into it
- Water Science and Technology — 16 citations into it
- Analytical Chemistry — 12 citations into it; #928 of 1,254 by trust here, #658 by citations
- Statistics and Probability — 9 citations into it
- Optimization & Training — 7 citations into it
Whom José Padarian trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- David M. Blei
- Jerome H. Friedman
- Yoshua Bengio
- Jianhua Lin
- Geoffrey E. Hinton
- James Bergstra
- Yann LeCun
- Michael I. Jordan
- Yarin Gal
- R. V. Rossel
Who trusts José Padarian overall
Papers from José Padarian's perspective
Ordered by how strongly the researchers José Padarian 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)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Convolutional neural network for simultaneous prediction of several soil properties using visible/near-infrared, mid-infrared, and their combined spectra (2019) — 407 citations
- Machine learning and soil sciences: a review aided by machine learning tools (2019) — 375 citations
- Using deep learning to predict soil properties from regional spectral data (2019) — 387 citations
- Modeling Soil Processes: Review, Key Challenges, and New Perspectives (2016) — 696 citations