Joachim Sicking

Optimization & Training · 2018

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

Subfields

• Optimization & Training — 14 citations into it

Whom Joachim Sicking trusts overall

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

1. L. Eon Bottou
2. Yann LeCun

Papers from Joachim Sicking's perspective

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

1. The mnist database of handwritten digits
2. Stochastic Gradient Learning in Neural Networks
3. Optimal Distributed Online Prediction Using Mini-Batches
4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
5. ImageNet classification with deep convolutional neural networks
6. Gradient-based learning applied to document recognition
7. Long Short-Term Memory
8. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Efficient Decentralized Deep Learning by Dynamic Model Averaging (2018) — 131 citations