Maximilian Karl

Artificial Intelligence · 2015–2017

A researcher in the Science corpus: 4 papers; trusts 44 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 54 citations into it; #13,402 of 35,345 by trust here, #9,465 by citations
• Reinforcement Learning — 35 citations into it
• Probabilistic & Bayesian Methods — 24 citations into it
• Generative Models — 17 citations into it; #1,240 of 1,956 by trust here, #1,054 by citations
• Computer Vision and Pattern Recognition — 16 citations into it
• Control and Systems Engineering — 15 citations into it
• Optimization & Training — 10 citations into it
• Computational Theory and Mathematics — 9 citations into it

Whom Maximilian Karl trusts overall

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

1. Diederik P. Kingma
2. Alex Graves
3. Geoffrey E. Hinton
4. Danilo Jimenez Rezende
5. Yoshua Bengio
6. Radford M. Neal
7. Max Welling
8. J. Urgen Schmidhuber
9. Alex Krizhevsky
10. Yann LeCun

Who trusts Maximilian Karl overall

1. Danijar Hafner
2. Hao Wang
3. Timothée Lesort
4. Joseph Marino
5. Nan Rosemary Ke
6. Timothy P. Lillicrap
7. Diederik P. Kingma
8. Jimmy Ba
9. Naoya Takeishi
10. Daehyung Park

Papers from Maximilian Karl's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data (2016) — 417 citations
• Unsupervised Real-Time Control Through Variational Empowerment (2017) — 60 citations
• Stable reinforcement learning with autoencoders for tactile and visual data (2016) — 185 citations
• Fast Adaptive Weight Noise (2015) — 3 citations