Gregory Ditzler

Artificial Intelligence · 2013–2015

A researcher in the Science corpus: 5 papers; trusts 31 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 59 citations into it; #8,071 of 35,345 by trust here, #13,353 by citations
• Molecular Biology — 27 citations into it
• Information Systems — 13 citations into it
• Optimization & Training — 10 citations into it
• Evaluation & Benchmarks — 9 citations into it
• Computer Vision and Pattern Recognition — 8 citations into it; #17,196 of 21,575 by trust here, #20,271 by citations
• Management Science and Operations Research — 7 citations into it; #2,012 of 11,946 by trust here, #2,873 by citations
• Statistical Learning Theory — 6 citations into it

Whom Gregory Ditzler trusts overall

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

1. Ludmila I. Kuncheva
2. Geoffrey E. Hinton
3. Leo Breiman
4. Robert Tibshirani
5. Weizhong Li
6. Robi Polikar
7. Albert Bifet
8. Yoshua Bengio
9. W. Street
10. Radford M. Neal

Who trusts Gregory Ditzler overall

1. Andrea Dal Pozzolo
2. Alexander Rainer Tassilo Gepperth
3. Barbara Hammer
4. Viktor Losing
5. Adeleh Bitarafan
6. Gianluca Bontempi
7. Jie Lu
8. Alberto Fernández
9. Sarah Nogueira
10. Geoffrey I. Webb

Papers from Gregory Ditzler's perspective

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

Their papers

• Learning in Nonstationary Environments: A Survey (2015) — 778 citations
• Incremental Learning of Concept Drift from Streaming Imbalanced Data (2013) — 385 citations
• Fizzy: feature subset selection for metagenomics (2015) — 49 citations
• Multi-Layer and Recursive Neural Networks for Metagenomic Classification (2015) — 88 citations
• A Bootstrap Based Neyman-Pearson Test for Identifying Variable Importance (2015) — 27 citations