Leandro L. Minku
Artificial Intelligence · 2010–2017
A researcher in the Science corpus: 3 papers; trusts 31 researchers and is trusted by 13 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 81 citations into it; #11,270 of 35,345 by trust here, #11,750 by citations
- Information Systems — 14 citations into it
- Management Science and Operations Research — 13 citations into it; #3,308 of 11,946 by trust here, #3,047 by citations
- Computer Vision and Pattern Recognition — 11 citations into it
- Signal Processing — 10 citations into it
- Evaluation & Benchmarks — 9 citations into it
- Meta & Continual Learning — 8 citations into it
Whom Leandro L. Minku trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- J. Kolter
- Nikunj C. Oza
- Ludmila I. Kuncheva
- Leo Breiman
- J. Branke
- Paul E. Utgoff
- Jaka Demsar
- M. Maloof
- Arthur U. Asuncion
- Albert Bifet
Who trusts Leandro L. Minku overall
- Heitor Murilo Gomes
- Jean Paul Barddal
- Albert Bifet
- Lior Rokach
- M. A. Ganaie
- Omer Sagi
- Fabrício Enembreck
- Indrė Žliobaitė
- Firas Bayram
- Xin Yao
Papers from Leandro L. Minku's perspective
Ordered by how strongly the researchers Leandro L. Minku trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Bagging Predictors
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- The Impact of Diversity on Online Ensemble Learning in the Presence of Concept Drift (2010) — 477 citations
- DDD: A New Ensemble Approach for Dealing with Concept Drift (2012) — 441 citations
- Ensemble learning for data stream analysis: A survey (2017) — 990 citations