Richard Kurle
Artificial Intelligence · 2019–2020
A researcher in the Science corpus: 2 papers; trusts 45 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 36 citations into it
- Computer Vision and Pattern Recognition — 25 citations into it
- Probabilistic & Bayesian Methods — 21 citations into it
- Generative Models — 17 citations into it
- Representation & Self-Supervised Learning — 13 citations into it
- Optimization & Training — 8 citations into it
- Meta & Continual Learning — 5 citations into it
Whom Richard Kurle trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Diederik P. Kingma
- Alex Graves
- David John Cameron MacKay
- Kaiming He
- Yoshua Bengio
- Manfred Opper
- Radford M. Neal
- Yann LeCun
- Charles Blundell
Who trusts Richard Kurle overall
Papers from Richard Kurle's perspective
Ordered by how strongly the researchers Richard Kurle 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)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- 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
- Learning Hierarchical Priors in VAEs (2019) — 110 citations
- Continual Learning with Bayesian Neural Networks for Non-Stationary Data (2020) — 82 citations