Martin Binder
Artificial Intelligence · WIREs Data. Mining. Knowl. Discov. · 2019–2021
A researcher in the Science corpus: 3 papers; trusts 12 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 114 citations into it
- Computational Theory and Mathematics — 37 citations into it
- Optimization & Training — 33 citations into it
- Management Science and Operations Research — 17 citations into it
- Neural Architecture Search & AutoML — 16 citations into it
- Safety, Robustness & Interpretability — 11 citations into it
Whom Martin Binder trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Frank Hutter
- Diederik P. Kingma
- Geoffrey E. Hinton
- Jasper Snoek
- James Bergstra
- Yoshua Bengio
- Bernd Bischl
- Alex Krizhevsky
- Donald R. Jones
- Marius Thomas Lindauer
Papers from Martin Binder's perspective
Ordered by how strongly the researchers Martin Binder trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Gradient-based learning applied to document recognition
- Learning Multiple Layers of Features from Tiny Images
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Going deeper with convolutions
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges (2021) — 1,013 citations
- Multi-Objective Counterfactual Explanations (2020) — 322 citations
- mlr3: A modern object-oriented machine learning framework in R (2019) — 406 citations