Henrik Marklund
Artificial Intelligence · 2019–2020
A researcher in the Science corpus: 3 papers; trusts 77 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 246 citations into it
- Computer Vision and Pattern Recognition — 128 citations into it
- Evaluation & Benchmarks — 56 citations into it
- Representation & Self-Supervised Learning — 51 citations into it
- Radiology, Nuclear Medicine and Imaging — 38 citations into it
- Optimization & Training — 12 citations into it
- Model Architectures — 8 citations into it
Whom Henrik Marklund trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Geoffrey E. Hinton
- Kaiming He
- Yann LeCun
- Yoshua Bengio
- Diederik P. Kingma
- Ilya Sutskever
- Christian Szegedy
- J. Urgen Schmidhuber
- Sergey Ioffe
Who trusts Henrik Marklund overall
Papers from Henrik Marklund's perspective
Ordered by how strongly the researchers Henrik Marklund 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)
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020) — 1,913 citations
- CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison (2019) — 3,612 citations
- Adaptive Risk Minimization: A Meta-Learning Approach for Tackling Group Shift (2020) — 88 citations