Kristin P. Bennett

Artificial Intelligence · 1992–2015

A researcher in the Science corpus: 20 papers; trusts 55 researchers and is trusted by 58 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 53 citations into it; #4,760 of 35,345 by trust here, #8,283 by citations
• Statistical Learning Theory — 31 citations into it
• Optimization & Training — 26 citations into it
• Computer Vision and Pattern Recognition — 21 citations into it; #4,009 of 21,575 by trust here, #6,954 by citations
• Computational Theory and Mathematics — 14 citations into it; #2,746 of 12,182 by trust here, #2,565 by citations
• Statistics and Probability — 14 citations into it
• Control and Systems Engineering — 13 citations into it
• Evaluation & Benchmarks — 12 citations into it

Whom Kristin P. Bennett trusts overall

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

1. Bernhard Schölkopf
2. Thorsten Joachims
3. Vladimir Naumovich Vapnik
4. Vladimir Naumovich Vapni
5. Olvi L. Mangasarian
6. Alex Smola
7. Nello Cristianini
8. Leo Breiman
9. John Shawe-Taylor
10. Geoffrey E. Hinton

Who trusts Kristin P. Bennett overall

1. Olvi L. Mangasarian
2. Emilio Carrizosa
3. W. Street
4. TreesKristin P. Bennett
5. Bernhard Schölkopf
6. Alex Smola
7. Dolores Romero Morales
8. S. Sathiya Keerthi
9. Claudio Gambella
10. Jennifer A. BlueDepartment

Papers from Kristin P. Bennett's perspective

Ordered by how strongly the researchers Kristin P. Bennett 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Linear Programming Boosting via Column Generation (2002) — 481 citations
• Column-generation boosting methods for mixture of kernels (2004) — 96 citations
• Multiple instance ranking (2008) — 46 citations
• Bilevel Optimization and Machine Learning (2008) — 70 citations
• A Pattern Search Method for Model Selection of Support Vector Regression (2002) — 177 citations
• Support vector machines: hype or hallelujah? (2000) — 766 citations
• Fast Bundle Algorithm for Multiple-Instance Learning (2012) — 75 citations
• Design of the 2015 ChaLearn AutoML challenge (2015) — 135 citations
• Global Tree Optimization: A Non-greedy Decision Tree Algorithm (2007) — 84 citations
• The Interplay of Optimization and Machine Learning Research (2006) — 168 citations
• Building Support Vector Machines with Reduced Classifier Complexity (2006) — 318 citations
• Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining (2005) — 133 citations
• Regression Error Characteristic Curves (2003) — 304 citations
• Dimensionality Reduction via Sparse Support Vector Machines (2003) — 492 citations
• Strip mining for molecules (2002)
• Duality and Geometry in SVM Classifiers (2000) — 430 citations
• Constrained K-Means Clustering (2000) — 521 citations
• Bilinear separation of two sets inn-space (1993)
• Robust linear programming discrimination of two linearly inseparable sets (1992)
• Decision Tree Construction Via Linear Programming (1992)