Jesse Read
Artificial Intelligence · 2008–2018
A researcher in the Science corpus: 7 papers; trusts 43 researchers and is trusted by 33 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 54 citations into it; #5,808 of 35,345 by trust here, #7,657 by citations
- Evaluation & Benchmarks — 21 citations into it
- Information Systems — 19 citations into it; #726 of 8,771 by trust here, #968 by citations
- Molecular Biology — 14 citations into it
- Computer Vision and Pattern Recognition — 13 citations into it
- Signal Processing — 12 citations into it
- Statistical Learning Theory — 8 citations into it
- Management Science and Operations Research — 7 citations into it
Whom Jesse Read trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Grigorios Tsoumakas
- Robert E. Schapire
- Min-Ling Zhang
- Jaka Demsar
- Albert Bifet
- Shantanu Godbole
- Weiwei Cheng
- G. Holmes
- Johannes Fürnkranz
- Bernhard Pfahringer
Who trusts Jesse Read overall
- Min-Ling Zhang
- E. Gibaja Galindo
- Konstantinos Sechidis
- Francisco Charte
- A. D. Sá
- Gjorgji Madjarov
- Krzysztof Dembczynski
- Sun-Yuan Kung
- Grigorios Tsoumakas
- Sebastián Ventura
Papers from Jesse Read's perspective
Ordered by how strongly the researchers Jesse Read 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)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Classifier chains for multi-label classification (2009) — 2,723 citations
- On the flexibility of the design of multiple try Metropolis schemes (2012) — 64 citations
- Scikit-Multiflow: A Multi-output Streaming Framework (2018) — 349 citations
- Adaptive random forests for evolving data stream classification (2017) — 763 citations
- MEKA: A Multi-label/Multi-target Extension to WEKA (2016) — 239 citations
- Scalable and efficient multi-label classification for evolving data streams (2012) — 147 citations
- Multi-label Classification Using Ensembles of Pruned Sets (2008) — 475 citations