Mark J. Embrechts

Artificial Intelligence · 2000–2013

A researcher in the Science corpus: 7 papers; trusts 31 researchers and is trusted by 31 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 21 citations into it; #5,960 of 35,345 by trust here, #14,718 by citations
• Evaluation & Benchmarks — 9 citations into it
• Statistical Learning Theory — 9 citations into it
• Statistics and Probability — 9 citations into it
• Information Systems — 7 citations into it; #2,327 of 8,771 by trust here, #5,501 by citations
• Computer Vision and Pattern Recognition — 6 citations into it; #6,562 of 21,575 by trust here, #12,689 by citations
• Model Architectures — 5 citations into it
• Optimization & Training — 5 citations into it

Whom Mark J. Embrechts trusts overall

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

1. Paulo Cortez
2. Tom Fawcett
3. Eric R. Ziegel
4. R. Kewley
5. Paul J. Werbos
6. Dennis W. Ruck
7. Martin Fodslette Møller
8. Thomas G. Dietterich
9. Honghua (Kathy) Dai
10. Leo Breiman

Who trusts Mark J. Embrechts overall

1. Paulo Cortez
2. Sérgio Moro
3. Amina Adadi
4. Riccardo Guidotti
5. Vaishak Belle
6. Josua Krause
7. Mohammed Berrada
8. Rei Sonobe
9. H. Christopher Frey
10. Paulo Rita

Papers from Mark J. Embrechts's perspective

Ordered by how strongly the researchers Mark J. Embrechts 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. Gradient-based learning applied to document recognition
4. The Nature of Statistical Learning Theory
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Beyond Regression : "New Tools for Prediction and Analysis in the Behavioral Sciences
7. A training algorithm for optimal margin classifiers
8. Neural Networks for Pattern Recognition
9. ImageNet: A large-scale hierarchical image database
10. Long Short-Term Memory

Their papers

• Data strip mining for the virtual design of pharmaceuticals with neural networks (2000) — 152 citations
• Data Mining for Molecules with 2-D Neural Network Sensitivity Analysis (2003) — 13 citations
• Using sensitivity analysis and visualization techniques to open black box data mining models (2013) — 428 citations
• Opening black box Data Mining models using Sensitivity Analysis (2011) — 113 citations
• On the Use of the Adjusted Rand Index as a Metric for Evaluating Supervised Classification (2009) — 488 citations
• Dimensionality Reduction via Sparse Support Vector Machines (2003) — 492 citations
• Strip mining for molecules (2002)