Daniela Korhammer

Probabilistic & Bayesian Methods · 2015

A researcher in the Science corpus: 1 paper; trusts 11 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Probabilistic & Bayesian Methods — 9 citations into it
• Artificial Intelligence — 6 citations into it
• Optimization & Training — 6 citations into it

Whom Daniela Korhammer trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Diederik P. Kingma
2. Alex Graves
3. Jorma Rissanen
4. Geoffrey E. Hinton
5. Radford M. Neal
6. David John Cameron MacKay
7. Sida I. Wang
8. Simon Justin Julier

Papers from Daniela Korhammer's perspective

Ordered by how strongly the researchers Daniela Korhammer trusts endorse each paper. Same corpus, a different order for every researcher.

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. Auto-Encoding Variational Bayes (International Conference on Learning Representations)
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. ImageNet classification with deep convolutional neural networks
5. Gradient-based learning applied to document recognition
6. Generating Sequences With Recurrent Neural Networks
7. Improving neural networks by preventing co-adaptation of feature detectors
8. Long Short-Term Memory
9. Attention is All you Need (Neural Information Processing Systems)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Fast Adaptive Weight Noise (2015) — 3 citations