Andreas Kirsch

Artificial Intelligence · with Liu · 2009–2021

A researcher in the Science corpus: 5 papers; trusts 90 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 50 citations into it; #21,775 of 35,345 by trust here, #23,731 by citations
• Computer Vision and Pattern Recognition — 26 citations into it
• Probabilistic & Bayesian Methods — 16 citations into it
• Optimization & Training — 9 citations into it
• Evaluation & Benchmarks — 7 citations into it
• Representation & Self-Supervised Learning — 6 citations into it
• Generative Models — 5 citations into it
• Model Architectures — 5 citations into it

Whom Andreas Kirsch trusts overall

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

1. Diederik P. Kingma
2. Yarin Gal
3. Kaiming He
4. Alex Krizhevsky
5. Karen Simonyan
6. Geoffrey E. Hinton
7. Yann LeCun
8. Yoshua Bengio
9. Zoubin Ghahramani
10. Jimmy Ba

Who trusts Andreas Kirsch overall

1. Zalán Borsos
2. Samuel Budd
3. Bertie Vidgen
4. Pengzhen Ren
5. Moloud Abdar
6. Mojm'ir Mutn'y
7. Emma Claire Robinson
8. Leon Derczynski
9. Yun Xiao

Papers from Andreas Kirsch's perspective

Ordered by how strongly the researchers Andreas Kirsch 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. 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. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning (2019) — 777 citations
• Deterministic Neural Networks with Appropriate Inductive Biases Capture Epistemic and Aleatoric Uncertainty (2021) — 99 citations
• The large enriched germanium experiment for neutrinoless double beta decay (LEGEND) (2017) — 257 citations
• The Gerda experiment for the search of 0νββ decay in 76Ge (2012) — 170 citations
• Metallic magnetic calorimeters (2009) — 214 citations