Niklas Pfister

Artificial Intelligence · 2016–2020

A researcher in the Science corpus: 5 papers; trusts 86 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 93 citations into it; #21,906 of 35,345 by trust here, #19,018 by citations
• Statistics and Probability — 86 citations into it; #5,489 of 12,932 by trust here, #3,893 by citations
• Causal Inference & Discovery — 58 citations into it
• Molecular Biology — 45 citations into it
• Probabilistic & Bayesian Methods — 32 citations into it
• Control and Systems Engineering — 18 citations into it
• Statistical and Nonlinear Physics — 18 citations into it
• Statistical Learning Theory — 16 citations into it; #805 of 1,038 by trust here, #678 by citations

Whom Niklas Pfister trusts overall

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

1. Jan Peters
2. Arthur Gretton
3. Bernhard Schölkopf
4. Peter Buhlmann
5. Dominik Janzing
6. Robert Tibshirani
7. Kenji Fukumizu
8. Alex Smola
9. Joris M. Mooij
10. Peter M. Bentler

Who trusts Niklas Pfister overall

1. Bernhard Schölkopf
2. Jan Peters
3. Stefan Bauer
4. Thomas B. Berrett
5. Francesco Locatello
6. Arin Chaudhuri
7. Mathias Drton
8. Shu Yao
9. Fei Ye
10. Yoshua Bengio

Papers from Niklas Pfister's perspective

Ordered by how strongly the researchers Niklas Pfister 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. Learning Multiple Layers of Features from Tiny Images
9. GENERATIVE ADVERSARIAL NETS
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Kernel‐based tests for joint independence (2016) — 224 citations
• Causal Models for Dynamical Systems (2020) — 58 citations
• Invariant Causal Prediction for Sequential Data (2017) — 150 citations
• Identifying Causal Structure in Large-Scale Kinetic Systems (2018) — 9 citations
• Learning stable and predictive structures in kinetic systems (2018) — 46 citations