Lars Kai Hansen

Artificial Intelligence · 1990–2019

A researcher in the Science corpus: 19 papers; trusts 888 researchers and is trusted by 43 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 199 citations into it; #8,568 of 35,345 by trust here, #6,614 by citations
• Computer Vision and Pattern Recognition — 152 citations into it; #10,618 of 21,575 by trust here, #10,154 by citations
• Safety, Robustness & Interpretability — 97 citations into it
• Cognitive Neuroscience — 51 citations into it; #6,077 of 10,852 by trust here, #4,819 by citations
• Model Architectures — 38 citations into it; #749 of 2,278 by trust here, #999 by citations
• Natural Language Processing — 29 citations into it
• Biophysics — 26 citations into it
• Optimization & Training — 26 citations into it

Whom Lars Kai Hansen trusts overall

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

1. Karen Simonyan
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Diederik P. Kingma
5. Matthew D. Zeiler
6. Yoshua Bengio
7. Kaiming He
8. Christian Szegedy
9. Yann LeCun
10. Andrew Zisserman

Who trusts Lars Kai Hansen overall

1. Thomas G. Dietterich
2. Naonori Ueda
3. J. D. de Winter
4. Jack Hanson
5. Finn Årup Nielsen
6. Zhi-Hua Zhou
7. Yongquan Yang
8. Dimitra Dodou
9. Laura Rieger
10. Matthew C. Tresch

Papers from Lars Kai Hansen's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Dreaming More Data: Class-dependent Distributions over Diffeomorphisms for Learned Data Augmentation (2015) — 160 citations
• Latent Space Oddity: on the Curvature of Deep Generative Models (2017) — 331 citations
• Structuring Neural Networks for More Explainable Predictions (2018) — 15 citations
• Probably Approximately Correct Search (2009) — 22 citations
• Infinite multiple membership relational modeling for complex networks (2011) — 64 citations
• Good Friends, Bad News - Affect and Virality in Twitter (2011) — 411 citations
• Detection of skin cancer by classification of Raman spectra (2004) — 304 citations
• Explainable AI: Interpreting, Explaining and Visualizing Deep Learning (2019) — 1,312 citations
• Aggregating explanation methods for stable and robust explainability. (2019) — 16 citations
• Aggregating explainability methods for neural networks stabilizes explanations (2019) — 12 citations
• Deep convolutional neural networks for interpretable analysis of EEG sleep stage scoring (2017) — 150 citations
• Visualization of Nonlinear Classification Models in Neuroimaging - Signed Sensitivity Maps (2012) — 22 citations
• Model sparsity and brain pattern interpretation of classification models in neuroimaging (2012) — 144 citations
• Visualization of nonlinear kernel models in neuroimaging by sensitivity maps (2011) — 75 citations
• Archetypal analysis for machine learning (2010) — 207 citations
• A New Database for Speaker Recognition (2005) — 48 citations
• Blind detection of independent dynamic components (2001) — 84 citations
• Adaptive Regularization in Neural Network Modeling (1996)
• Neural Network Ensembles (1990)