Alexander Rauscher

Artificial Intelligence · 2017

A researcher in the Science corpus: 1 paper; trusts 2 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 6 citations into it
• Computer Vision and Pattern Recognition — 6 citations into it

Whom Alexander Rauscher trusts

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

1. Geoffrey E. Hinton (in Probabilistic & Bayesian Methods)
2. Alex Krizhevsky (in Computer Vision and Pattern Recognition)

Papers from Alexander Rauscher's perspective

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

1. Learning Multiple Layers of Features from Tiny Images
2. A Fast Learning Algorithm for Deep Belief Nets
3. Multimodal Deep Learning
4. A Practical Guide to Training Restricted Boltzmann Machines
5. Random Forests
6. ADADELTA: An Adaptive Learning Rate Method
7. Deep learning for robust feature generation in audiovisual emotion recognition (IEEE International Conference on Acoustics, Speech, and Signal Processing)
8. N4ITK: Improved N3 Bias Correction
9. Alzheimer's disease. (Cold Spring Harbor Perspectives in Biology)
10. ImageNet classification with deep convolutional neural networks

Their papers

• Deep learning of joint myelin and T1w MRI features in normal-appearing brain tissue to distinguish between multiple sclerosis patients and healthy controls (2017) — 88 citations