Stephan Rabanser
Artificial Intelligence · 2018
A researcher in the Science corpus: 1 paper; trusts 33 researchers and is trusted by 3 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 33 citations into it; #26,687 of 35,345 by trust here, #25,040 by citations
- Computer Vision and Pattern Recognition — 15 citations into it
- Safety, Robustness & Interpretability — 10 citations into it
- Statistics and Probability — 8 citations into it
- Computer Networks and Communications — 5 citations into it
Whom Stephan Rabanser trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Aaditya Ramdas
- Kaiming He
- Bernhard Schölkopf
- Geoffrey E. Hinton
- T. Loughin
- Alex Krizhevsky
- Ian J. Goodfellow
- Arthur Gretton
- Alex Smola
- Ilya Sutskever
Who trusts Stephan Rabanser overall
- Shira Mitchell
- Irene Y. Chen
- Jean Feng
- Eric Potash
- Bernhard Schölkopf
- Solon Barocas
- Emma Pierson
- Rachael V. Phillips
Papers from Stephan Rabanser's perspective
Ordered by how strongly the researchers Stephan Rabanser 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
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift (2018) — 473 citations