Jilles Vreeken

Artificial Intelligence · 2011–2017

A researcher in the Science corpus: 6 papers; trusts 43 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 37 citations into it; #8,948 of 35,345 by trust here, #21,316 by citations
• Causal Inference & Discovery — 31 citations into it
• Computational Theory and Mathematics — 27 citations into it; #2,180 of 12,182 by trust here, #5,046 by citations
• Information Theory — 18 citations into it
• Probabilistic & Bayesian Methods — 13 citations into it
• Statistics and Probability — 13 citations into it; #9,121 of 12,932 by trust here, #11,487 by citations
• Information Systems — 9 citations into it; #2,067 of 8,771 by trust here, #6,195 by citations
• Statistical Learning Theory — 9 citations into it

Whom Jilles Vreeken trusts overall

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

1. Jorma Rissanen
2. Dominik Janzing
3. Jan Peters
4. Peter D. Grünwald
5. Bernhard Schölkopf
6. Joris M. Mooij
7. Tom L. Burr
8. J. Ross Quinlan
9. Kun Zhang
10. Eleni Sgouritsa

Who trusts Jilles Vreeken overall

1. Christian Borgelt
2. Niki Kilbertus
3. Michael Mampaey
4. David S. Matteson
5. Nicholas A. James
6. Roel Bertens
7. Bernhard Schölkopf
8. Andreas Henelius
9. Alexander Marx
10. Jovana Mitrovi'c

Papers from Jilles Vreeken's perspective

Ordered by how strongly the researchers Jilles Vreeken 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Learning Multiple Layers of Features from Tiny Images
7. Attention is All you Need (Neural Information Processing Systems)
8. GENERATIVE ADVERSARIAL NETS
9. ImageNet: A large-scale hierarchical image database
10. Et al

Their papers

• Causal Inference by Stochastic Complexity (2017) — 9 citations
• Origo: causal inference by compression (2016) — 67 citations
• Telling Cause from Effect Using MDL-Based Local and Global Regression (2017) — 71 citations
• Krimp: mining itemsets that compress (2011) — 329 citations
• Summarizing categorical data by clustering attributes (2011) — 32 citations
• Keeping it Short and Simple: Summarising Complex Event Sequences with Multivariate Patterns (2015) — 39 citations