Robi Polikar

Artificial Intelligence · 2001–2018

A researcher in the Science corpus: 11 papers; trusts 81 researchers and is trusted by 58 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 107 citations into it; #3,447 of 35,345 by trust here, #4,979 by citations
• Molecular Biology — 25 citations into it
• Information Systems — 24 citations into it
• Safety, Robustness & Interpretability — 21 citations into it
• Optimization & Training — 19 citations into it; #1,876 of 2,601 by trust here, #894 by citations
• Signal Processing — 18 citations into it
• Evaluation & Benchmarks — 16 citations into it
• Statistical Learning Theory — 16 citations into it

Whom Robi Polikar trusts overall

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

1. Ludmila I. Kuncheva
2. Robert E. Schapire
3. Yoav Freund
4. Robert A. Jacobs
5. Michael I. Jordan
6. Leo Breiman
7. Tin Kam Ho
8. Thierry Denoeux
9. Geoffrey E. Hinton
10. Nick Littlestone

Who trusts Robi Polikar overall

1. Vukosi Marivate
2. Xiaodong Li
3. Andrea Dal Pozzolo
4. Linyuan Lu
5. Shervin Malmasi
6. Shan Li
7. Gregory Ditzler
8. Fei Ye
9. Alexander Rainer Tassilo Gepperth
10. Varun Ojha

Papers from Robi Polikar's perspective

Ordered by how strongly the researchers Robi Polikar 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

• Attack Strength vs. Detectability Dilemma in Adversarial Machine Learning (2018) — 36 citations
• Learning in Nonstationary Environments: A Survey (2015) — 778 citations
• Learn++: an incremental learning algorithm for supervised neural networks (2001) — 946 citations
• Incremental Learning of Concept Drift in Nonstationary Environments (2011) — 930 citations
• Incremental Learning of Concept Drift from Streaming Imbalanced Data (2013) — 385 citations
• Neural network-based taxonomic clustering for metagenomics (2010) — 4 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
• IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2015) — 46 citations
• Learn$^{++}$ .NC: Combining Ensemble of Classifiers With Dynamically Weighted Consult-and-Vote for Efficient Incremental Learning of New Classes (2009) — 226 citations
• Ensemble based systems in decision making (2006)