Fredrik Lindsten
Artificial Intelligence · 2013–2019
A researcher in the Science corpus: 9 papers; trusts 100 researchers and is trusted by 21 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 75 citations into it; #6,349 of 35,345 by trust here, #12,387 by citations
- Probabilistic & Bayesian Methods — 70 citations into it; #1,489 of 1,525 by trust here, #1,220 by citations
- Statistics and Probability — 44 citations into it; #5,120 of 12,932 by trust here, #5,661 by citations
- Statistical Learning Theory — 12 citations into it
- Management Science and Operations Research — 12 citations into it
- Optimization & Training — 11 citations into it; #1,778 of 2,601 by trust here, #2,112 by citations
- Computer Vision and Pattern Recognition — 9 citations into it; #13,050 of 21,575 by trust here, #16,787 by citations
- Global and Planetary Change — 9 citations into it
Whom Fredrik Lindsten trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Arnaud Doucet
- Michael K. Pitt
- Haikady N. Nagaraja
- Fredrik K. Gustafsson
- Greg C. G. Wei
- Arthur P. Dempster
- Christophe Andrieu
- Simon J. Godsill
- Thomas Bo Schön
- Albert Benveniste
Who trusts Fredrik Lindsten overall
- Mingming Gong
- Andreas Svensson
- Kun Zhang
- Thomas Bo Schön
- C. A. Naesseth
- Yura N. Perov
- Peter J. Green
- Biwei Huang
- Bernhard Schölkopf
- Tom Rainforth
Papers from Fredrik Lindsten's perspective
Ordered by how strongly the researchers Fredrik Lindsten 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
- Learning Multiple Layers of Features from Tiny Images
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- GENERATIVE ADVERSARIAL NETS
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Calibration tests in multi-class classification: A unifying framework (2019) — 120 citations
- Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC (2013) — 167 citations
- An efficient stochastic approximation EM algorithm using conditional particle filters (2013) — 56 citations
- Sequential Kernel Herding: Frank-Wolfe Optimization for Particle Filtering (2015) — 87 citations
- Nested Sequential Monte Carlo Methods (2015) — 88 citations
- Particle gibbs with ancestor sampling (2014) — 279 citations
- Interacting Particle Markov Chain Monte Carlo (2016) — 36 citations
- Evaluating model calibration in classification (2019) — 273 citations
- Identification of jump Markov linear models using particle filters (2014) — 30 citations