Michael S. Steinbach

Artificial Intelligence · 2000–2020

A researcher in the Science corpus: 13 papers; trusts 84 researchers and is trusted by 39 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 168 citations into it; #13,090 of 35,345 by trust here, #4,445 by citations
• Statistical and Nonlinear Physics — 149 citations into it; #3,758 of 10,127 by trust here, #4,820 by citations
• Computer Vision and Pattern Recognition — 70 citations into it
• Statistics and Probability — 70 citations into it
• Computational Mechanics — 59 citations into it
• Materials Chemistry — 58 citations into it
• Molecular Biology — 51 citations into it
• Computational Theory and Mathematics — 45 citations into it

Whom Michael S. Steinbach trusts overall

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

1. Anil K. Jain
2. Steven L. Brunton
3. Maizar Raissi
4. Geoffrey E. Hinton
5. Diederik P. Kingma
6. Kaiming He
7. J. Behler
8. Alex Krizhevsky
9. Yoshua Bengio
10. Igor Mezi'c

Who trusts Michael S. Steinbach overall

1. Pavel Berkhin
2. Pasi Fränti
3. Andreas Hotho
4. Ying Zhao
5. Liping Jing
6. Shi Zhong
7. Sami Sieranoja
8. Khaled M. Hammouda
9. Ella Rabinovich
10. Ingo Feinerer

Papers from Michael S. Steinbach's perspective

Ordered by how strongly the researchers Michael S. Steinbach trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. Et al

Their papers

• The Challenges of Clustering High Dimensional Data (2004) — 564 citations
• Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data (2016) — 1,347 citations
• Mining Electronic Health Records: A Survey (2017) — 87 citations
• Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems (2020) — 728 citations
• Integrating Physics-Based Modeling with Machine Learning: A Survey (2020) — 416 citations
• Process‐Guided Deep Learning Predictions of Lake Water Temperature (2019) — 329 citations
• Theory-Guided Data Science for Climate Change (2014) — 33 citations
• A graph‐based approach to find teleconnections in climate data (2013) — 43 citations
• Top 10 algorithms in data mining (2007) — 5,999 citations
• Introduction to Data Mining (2005) — 650 citations
• Introduction to Data Mining, (First Edition) (2005) — 1,614 citations
• Finding Clusters of Different Sizes, Shapes, and Densities in Noisy, High Dimensional Data (2003) — 785 citations
• A Comparison of Document Clustering Techniques (2000) — 3,316 citations