Fredrik D. Johansson
Artificial Intelligence · 2015–2019
A researcher in the Science corpus: 10 papers; trusts 308 researchers and is trusted by 53 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 135 citations into it; #4,645 of 35,345 by trust here, #6,014 by citations
- Statistics and Probability — 64 citations into it; #10,717 of 12,932 by trust here, #11,487 by citations
- Computer Vision and Pattern Recognition — 55 citations into it
- Representation & Self-Supervised Learning — 55 citations into it; #774 of 3,145 by trust here, #1,155 by citations
- Graph Learning — 37 citations into it
- Causal Inference & Discovery — 36 citations into it; #89 of 364 by trust here, #90 by citations
- Computational Theory and Mathematics — 29 citations into it
- Molecular Biology — 21 citations into it
Whom Fredrik D. Johansson trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Christopher K. I. Williams
- Yoshua Bengio
- Donald B. Rubin
- Geoffrey E. Hinton
- Karsten M. Borgwardt
- Arthur Gretton
- Shai Ben-David
- Yann LeCun
- Bernhard Schölkopf
- Alex Krizhevsky
Who trusts Fredrik D. Johansson overall
- Ahmed M. Alaa
- Mihaela van der Schaar
- Ioana Bica
- Ruocheng Guo
- Uri Shalit
- Christopher Morris
- Liuyi Yao
- Individualized Treat
- Onur Atan
- Carolyn Kim
Papers from Fredrik D. Johansson's perspective
Ordered by how strongly the researchers Fredrik D. Johansson trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Learning Representations for Counterfactual Inference (2016) — 844 citations
- Evaluating Reinforcement Learning Algorithms in Observational Health Settings (2018) — 151 citations
- Support and Invertibility in Domain-Invariant Representations (2019) — 177 citations
- A survey on graph kernels (2019) — 491 citations
- Neural context embeddings for automatic discovery of word senses (2015) — 26 citations
- Guidelines for reinforcement learning in healthcare (2019) — 449 citations
- Bounding and Minimizing Counterfactual Error (2016) — 11 citations
- Why Is My Classifier Discriminatory? (2018) — 448 citations
- Estimating individual treatment effect: generalization bounds and algorithms (2016) — 1,270 citations
- Learning with Similarity Functions on Graphs using Matchings of Geometric Embeddings (2015) — 42 citations