Natasa Tagasovska
Artificial Intelligence · 2018
A researcher in the Science corpus: 1 paper; trusts 93 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 44 citations into it
- Probabilistic & Bayesian Methods — 16 citations into it
- Computer Vision and Pattern Recognition — 13 citations into it
- Safety, Robustness & Interpretability — 10 citations into it
- Statistics and Probability — 10 citations into it
- Evaluation & Benchmarks — 7 citations into it
- Optimization & Training — 7 citations into it
- Causal Inference & Discovery — 6 citations into it
Whom Natasa Tagasovska trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Diederik P. Kingma
- Kaiming He
- Charles Blundell
- Karen Simonyan
- Alex Krizhevsky
- Yarin Gal
- Yoshua Bengio
- Yann LeCun
- Nicolai Meinshausen
Who trusts Natasa Tagasovska overall
Papers from Natasa Tagasovska's perspective
Ordered by how strongly the researchers Natasa Tagasovska 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
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Single-Model Uncertainties for Deep Learning (2018) — 321 citations