Jonas M. B. Haslbeck

Statistics and Probability · 2015–2016

A researcher in the Science corpus: 2 papers; trusts 31 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 21 citations into it; #9,362 of 12,932 by trust here, #10,275 by citations
• Artificial Intelligence — 18 citations into it; #25,308 of 35,345 by trust here, #31,361 by citations
• Experimental and Cognitive Psychology — 15 citations into it; #5,650 of 9,731 by trust here, #8,664 by citations
• Cognitive Neuroscience — 11 citations into it; #6,610 of 10,852 by trust here, #10,026 by citations
• Molecular Biology — 8 citations into it
• Statistical and Nonlinear Physics — 8 citations into it; #6,633 of 10,127 by trust here, #8,981 by citations
• Statistical Learning Theory — 6 citations into it
• Management Science and Operations Research — 6 citations into it

Whom Jonas M. B. Haslbeck trusts overall

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

1. B. Pfaff
2. Nicolai Meinshausen
3. Bradley Efron
4. Eric R. Ziegel
5. Michael I. Jordan
6. Robert Tibshirani
7. Leo Breiman
8. Trevor J. Hastie
9. Sacha Epskamp
10. Martin J. Wainwright

Who trusts Jonas M. B. Haslbeck overall

1. Ali Shojaie
2. Alexander P. Christensen
3. Emily B. Fox
4. Hudson F. Golino
5. Sacha Epskamp
6. Paul J Silvia

Papers from Jonas M. B. Haslbeck's perspective

Ordered by how strongly the researchers Jonas M. B. Haslbeck trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Regression Shrinkage and Selection via the Lasso
3. Gradient-based learning applied to document recognition
4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. ImageNet: A large-scale hierarchical image database
7. The Elements of Statistical Learning
8. Long Short-Term Memory
9. Learning Multiple Layers of Features from Tiny Images
10. Attention is All you Need (Neural Information Processing Systems)

Their papers

• mgm: Estimating Time-Varying Mixed Graphical Models in High-Dimensional Data (2015) — 770 citations
• How well do network models predict observations? On the importance of predictability in network models (2016) — 532 citations