Marco Loog

Artificial Intelligence · 2001–2019

A researcher in the Science corpus: 13 papers; trusts 469 researchers and is trusted by 27 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 184 citations into it; #10,017 of 35,345 by trust here, #13,353 by citations
• Representation & Self-Supervised Learning — 91 citations into it
• Computer Vision and Pattern Recognition — 80 citations into it; #6,155 of 21,575 by trust here, #7,584 by citations
• Statistical Learning Theory — 36 citations into it
• Statistics and Probability — 28 citations into it; #5,301 of 12,932 by trust here, #6,080 by citations
• Natural Language Processing — 24 citations into it
• Causal Inference & Discovery — 22 citations into it
• Probabilistic & Bayesian Methods — 20 citations into it

Whom Marco Loog trusts overall

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

1. Thorsten Joachims
2. Bernhard Schölkopf
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Radford M. Neal
6. Alex Krizhevsky
7. Diederik P. Kingma
8. Yann LeCun
9. David Yarowsky
10. Robert P. W. Duin

Who trusts Marco Loog overall

1. Manli Zhu
2. Jesse H. Krijthe
3. Nojun Kwak
4. Amir Najafi
5. Dacheng Tao
6. Aleix M. Martinez
7. Alexander Mey
8. Carlos Eduardo Thomaz
9. Jingya Wang
10. Chao Yao

Papers from Marco Loog's perspective

Ordered by how strongly the researchers Marco Loog 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Semi-supervised learning, causality, and the conditional cluster assumption (2019) — 30 citations
• On classification with bags, groups and sets (2014) — 39 citations
• Linear dimensionality reduction via a heteroscedastic extension of LDA: the Chernoff criterion (2004) — 322 citations
• A Review of Domain Adaptation without Target Labels (2019) — 606 citations
• Semi-Generative Modelling: Covariate-Shift Adaptation with Cause and Effect Features (2018) — 15 citations
• Multiclass Linear Dimension Reduction by Weighted Pairwise Fisher Criteria (2001) — 483 citations
• Projected estimators for robust semi-supervised classification (2016) — 21 citations
• Improvability Through Semi-Supervised Learning: A Survey of Theoretical Results (2019) — 20 citations
• The Pessimistic Limits and Possibilities of Margin-based Losses in Semi-supervised Learning (2016) — 8 citations
• Contrastive Pessimistic Likelihood Estimation for Semi-Supervised Classification (2015) — 70 citations
• The Dipping Phenomenon (2012) — 26 citations
• Question Classification by Weighted Combination of Lexical, Syntactic and Semantic Features (2011) — 36 citations
• Comparative study of retinal vessel segmentation methods on a new publicly available database (2004) — 906 citations