Meelis Kull

Artificial Intelligence · 2007–2021

A researcher in the Science corpus: 9 papers; trusts 38 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 43 citations into it; #19,818 of 35,345 by trust here, #25,040 by citations
• Molecular Biology — 22 citations into it; #17,465 of 17,773 by trust here, #12,227 by citations
• Evaluation & Benchmarks — 19 citations into it; #2,220 of 3,147 by trust here, #3,134 by citations
• Information Systems — 12 citations into it
• Statistics and Probability — 11 citations into it; #7,951 of 12,932 by trust here, #10,855 by citations
• Management Science and Operations Research — 10 citations into it
• Computer Vision and Pattern Recognition — 8 citations into it
• Signal Processing — 7 citations into it

Whom Meelis Kull trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Jaka Demsar
2. Bianca Zadrozny
3. Charles Peter Elkan
4. Alex Smola
5. Trevor J. Hastie
6. Kaiming He
7. Yann LeCun
8. Peter A. Flach
9. Gao Huang
10. Mark A. Hall

Who trusts Meelis Kull overall

1. Eyke Hüllermeier
2. Ananya Kumar
3. Kyle Cranmer
4. Willem Waegeman
5. Jessa Bekker
6. Jonathan Wenger
7. Zhipeng Ding
8. Percy Liang
9. Rogelio Andrade Mancisidor
10. Fabian Küppers

Papers from Meelis Kull's perspective

Ordered by how strongly the researchers Meelis Kull trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Beyond temperature scaling: Obtaining well-calibrated multiclass probabilities with Dirichlet calibration (2019) — 547 citations
• Beyond sigmoids: How to obtain well-calibrated probabilities from binary classifiers with beta calibration (2017) — 128 citations
• g:Profiler—a web-based toolset for functional profiling of gene lists from large-scale experiments (2007) — 1,334 citations
• CRISP-DM Twenty Years Later: From Data Mining Processes to Data Science Trajectories (2021) — 337 citations
• Correlated daily time series and forecasting in the M4 competition (2020) — 8 citations
• Beta calibration: a well-founded and easily implemented improvement on logistic calibration for binary classifiers (2017) — 295 citations
• Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS 2017) (2017) — 6 citations
• Advances in Neural Information Processing Systems 28 (2015) — 178 citations
• Precision-Recall-Gain Curves: PR Analysis Done Right (2015) — 403 citations