Karen Ullrich
Artificial Intelligence · 2017
A researcher in the Science corpus: 2 papers; trusts 238 researchers and is trusted by 108 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 50 citations into it; #2,380 of 35,345 by trust here, #5,390 by citations
- Computer Vision and Pattern Recognition — 44 citations into it; #5,033 of 21,575 by trust here, #7,499 by citations
- Efficiency & Compression — 43 citations into it; #68 of 1,264 by trust here, #293 by citations
- Optimization & Training — 20 citations into it
- Model Architectures — 13 citations into it
- Probabilistic & Bayesian Methods — 12 citations into it
- Evaluation & Benchmarks — 6 citations into it
- Statistics and Probability — 6 citations into it
Whom Karen Ullrich trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yann LeCun
- Geoffrey E. Hinton
- Kaiming He
- Yoshua Bengio
- Alex Krizhevsky
- Alex Graves
- Jorma Rissanen
- Diederik P. Kingma
- Song Han
- Karen Simonyan
Who trusts Karen Ullrich overall
- Georgios Georgiadis
- Christos Louizos
- Dmitry Molchanov
- Tim Dettmers
- Max Welling
- Arsenii Ashukha
- Rahaf Aljundi
- Trevor Gale
- Simon Wiedemann
- Eirikur Agustsson
Papers from Karen Ullrich's perspective
Ordered by how strongly the researchers Karen Ullrich trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Going deeper with convolutions
Their papers
- Bayesian Compression for Deep Learning (2017) — 508 citations
- Soft Weight-Sharing for Neural Network Compression (2017) — 444 citations