Günter Klambauer

Artificial Intelligence · 2012–2024

A researcher in the Science corpus: 17 papers; trusts 367 researchers and is trusted by 57 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 230 citations into it; #7,705 of 35,345 by trust here, #4,602 by citations
• Computer Vision and Pattern Recognition — 198 citations into it; #9,903 of 21,575 by trust here, #5,620 by citations
• Computational Theory and Mathematics — 124 citations into it; #2,431 of 12,182 by trust here, #513 by citations
• Molecular Biology — 103 citations into it; #2,614 of 17,773 by trust here, #3,425 by citations
• Model Architectures — 82 citations into it; #604 of 2,278 by trust here, #468 by citations
• Materials Chemistry — 77 citations into it; #517 of 11,513 by trust here, #1,979 by citations
• Computational Chemistry & Drug Discovery — 75 citations into it; #403 of 425 by trust here, #294 by citations
• Representation & Self-Supervised Learning — 74 citations into it

Whom Günter Klambauer trusts overall

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

1. Geoffrey E. Hinton
2. Sepp Hochreiter
3. Yoshua Bengio
4. Alex Krizhevsky
5. Kaiming He
6. Ilya Sutskever
7. J. Urgen Schmidhuber
8. Yann LeCun
9. Diederik P. Kingma
10. Alex Graves

Who trusts Günter Klambauer overall

1. Garrett B. Goh
2. Kristina Preuer
3. Boris Hanin
4. Juri Opitz
5. George Philipp
6. Thomas Unterthiner
7. Bernhard Schäfl
8. Richard J. Chen
9. Andrew Brock
10. Prajit Ramachandran

Papers from Günter Klambauer's perspective

Ordered by how strongly the researchers Günter Klambauer 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• GANs Trained by a Two Time-Scale Update Rule Converge to a Nash Equilibrium (2017) — 506 citations
• Self-Normalizing Neural Networks (2017) — 2,892 citations
• Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields (2017) — 81 citations
• CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP (2021) — 129 citations
• Hopfield Networks is All You Need (2020) — 701 citations
• DeepTox: Toxicity Prediction using Deep Learning (2016) — 921 citations
• Toxicity Prediction using Deep Learning (2015) — 165 citations
• Modern Hopfield Networks and Attention for Immune Repertoire Classification (2020) — 148 citations
• Fréchet ChemNet Distance: A Metric for Generative Models for Molecules in Drug Discovery (2018) — 454 citations
• Large-scale comparison of machine learning methods for drug target prediction on ChEMBL (2018) — 442 citations
• DeepSynergy: predicting anti-cancer drug synergy with Deep Learning (2017) — 577 citations
• Improving Few- and Zero-Shot Reaction Template Prediction Using Modern Hopfield Networks (2022) — 90 citations
• xLSTM: Extended Long Short-Term Memory (2024) — 644 citations
• Modern Hopfield Networks for Few- and Zero-Shot Reaction Prediction (2021) — 10 citations
• Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets (2019) — 893 citations
• Fréchet ChemblNet Distance: A metric for generative models for molecules (2018) — 14 citations
• cn.MOPS: mixture of Poissons for discovering copy number variations in next-generation sequencing data with a low false discovery rate (2012) — 445 citations