Michael Brückner

Artificial Intelligence · 2007–2013

A researcher in the Science corpus: 7 papers; trusts 19 researchers and is trusted by 107 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 24 citations into it; #3,312 of 35,345 by trust here, #5,153 by citations
• Statistical Learning Theory — 12 citations into it
• Statistics and Probability — 8 citations into it
• Computational Theory and Mathematics — 6 citations into it
• Probabilistic & Bayesian Methods — 5 citations into it

Whom Michael Brückner trusts overall

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

1. Amir Globerson
2. Choon Hui Teo
3. Bernhard Schölkopf
4. Alex Smola
5. Sam T. Roweis
6. Laurent El Ghaoui
7. Tobias Scheffer
8. Bernard Walter Silverman
9. Gert R. G. Lanckriet
10. Andreas Wächter

Who trusts Michael Brückner overall

1. Battista Biggio
2. Bo Li
3. Yevgeniy Vorobeychik
4. Corinna Cortes
5. Jacob Steinhardt
6. Claudio Gambella
7. Scott Alfeld
8. MohamadAli Torkamani
9. Zhizhou Yin
10. Fabio Roli

Papers from Michael Brückner's perspective

Ordered by how strongly the researchers Michael Brückner 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. Attention is All you Need (Neural Information Processing Systems)
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
9. GENERATIVE ADVERSARIAL NETS
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Discriminative Learning Under Covariate Shift (2009) — 443 citations
• Discriminative learning for differing training and test distributions (2007) — 486 citations
• Static prediction games for adversarial learning problems (2012) — 216 citations
• Bayesian Games for Adversarial Regression Problems (2013) — 56 citations
• Machine Learning in Non-Stationary Environments - Introduction to Covariate Shift Adaptation (2012) — 434 citations
• Stackelberg games for adversarial prediction problems (2011) — 289 citations
• Nash Equilibria of Static Prediction Games (2009) — 91 citations