Frank Nielsen
Artificial Intelligence · with Nock · 2004–2019
A researcher in the Science corpus: 11 papers; trusts 125 researchers and is trusted by 49 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 67 citations into it; #11,278 of 35,345 by trust here, #16,471 by citations
- Statistics and Probability — 33 citations into it; #3,504 of 12,932 by trust here, #3,609 by citations
- Computer Vision and Pattern Recognition — 24 citations into it; #5,699 of 21,575 by trust here, #10,500 by citations
- Statistical Learning Theory — 17 citations into it
- Optimization & Training — 15 citations into it
- Scaling & Learning Theory — 14 citations into it
- Economics and Econometrics — 13 citations into it; #17,657 of 23,481 by trust here, #22,128 by citations
- Finance — 13 citations into it; #8,114 of 10,518 by trust here, #9,725 by citations
Whom Frank Nielsen trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Lawrence D. Brown
- Shun-ichi Amari
- Calyampudi R. Rao
- Jorma Rissanen
- Arindam Banerjee
- Arthur P. Dempster
- Yoshua Bengio
- Geoffrey E. Hinton
- Alex Smola
- Miroslav Lovric
Who trusts Frank Nielsen overall
- Gavin E. Crooks
- Giorgio Patrini
- Yuqing Zhu
- Xilun Chen
- Yu-Xiang Wang
- Rajesh Ranganath
- Christian Bienia
- Kai Li
- Stephen Hardy
- Claire Cardie
Papers from Frank Nielsen's perspective
Ordered by how strongly the researchers Frank Nielsen 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
- 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
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- DeepBach: a Steerable Model for Bach Chorales Generation (2016) — 494 citations
- Statistical region merging (2004) — 902 citations
- A closed-form expression for the Sharma–Mittal entropy of exponential families (2011) — 107 citations
- Local Polynomial Whittle Estimation of Perturbed Fractional Processes (2008) — 53 citations
- On the chi square and higher-order chi distances for approximating f-divergences (2013) — 136 citations
- Loss factorization, weakly supervised learning and label noise robustness (2016) — 120 citations
- K-variates++: More Pluses in the K-means++ (2016) — 24 citations
- Lightlike Neuromanifolds, Occam's Razor and Deep Learning (2019) — 13 citations
- Hypothesis Testing, Information Divergence and Computational Geometry (2013) — 18 citations
- Cramer-Rao Lower Bound and Information Geometry (2013) — 42 citations
- Bregman Divergences and Surrogates for Learning (2009) — 72 citations