Marthinus Christoffel du Plessis

Artificial Intelligence · 2012–2017

A researcher in the Science corpus: 4 papers; trusts 39 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 36 citations into it; #20,341 of 35,345 by trust here, #19,873 by citations
• Statistics and Probability — 18 citations into it
• Computer Vision and Pattern Recognition — 10 citations into it
• Optimization & Training — 9 citations into it
• Representation & Self-Supervised Learning — 9 citations into it
• Control and Systems Engineering — 6 citations into it
• Evaluation & Benchmarks — 6 citations into it
• Signal Processing — 5 citations into it; #5,297 of 7,572 by trust here, #6,355 by citations

Whom Marthinus Christoffel du Plessis trusts overall

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

1. Amor Keziou
2. Charles Peter Elkan
3. Masashi Sugiyama
4. Chih-Jen Lin
5. Bernard Walter Silverman
6. Gilles Blanchard
7. Ronan Collobert
8. Geoffrey J. McLachlan
9. Marco F. Duarte
10. Geoffrey E. Hinton

Who trusts Marthinus Christoffel du Plessis overall

1. Jessa Bekker
2. Ching-Yao Chuang
3. Jesse Davis
4. Danfei Xu
5. Joshua Robinson
6. Ryuichi Kiryo
7. Gang Niu
8. Giorgio Patrini
9. Sana Tonekaboni
10. Masashi Sugiyama

Papers from Marthinus Christoffel du Plessis's perspective

Ordered by how strongly the researchers Marthinus Christoffel du Plessis 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
8. ImageNet: A large-scale hierarchical image database
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Positive-Unlabeled Learning with Non-Negative Risk Estimator (2017) — 603 citations
• Semi-Supervised Learning of Class Balance under Class-Prior Change by Distribution Matching (2012) — 139 citations
• Convex Formulation for Learning from Positive and Unlabeled Data (2015) — 379 citations
• Analysis of Learning from Positive and Unlabeled Data (2014) — 438 citations