Nico Görnitz

Artificial Intelligence · 2011–2019

A researcher in the Science corpus: 7 papers; trusts 41 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 111 citations into it; #30,987 of 35,345 by trust here, #17,412 by citations
• Computer Vision and Pattern Recognition — 50 citations into it
• Representation & Self-Supervised Learning — 30 citations into it
• Computer Networks and Communications — 24 citations into it; #8,518 of 9,886 by trust here, #3,638 by citations
• Molecular Biology — 23 citations into it; #16,943 of 17,773 by trust here, #16,150 by citations
• Optimization & Training — 20 citations into it
• Statistical Learning Theory — 17 citations into it
• Probabilistic & Bayesian Methods — 14 citations into it

Whom Nico Görnitz trusts overall

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

1. Geoffrey E. Hinton
2. Bernhard Schölkopf
3. Alex Krizhevsky
4. Diederik P. Kingma
5. Yoshua Bengio
6. Olivier Chapelle
7. Thorsten Joachims
8. Yann LeCun
9. John Shawe-Taylor
10. Vladimir Naumovich Vapnik

Who trusts Nico Görnitz overall

1. Lukas Ruff
2. Tolga Ergen
3. Raghavendra Chalapathy
4. Guansong Pang
5. Curtis G. Northcutt
6. Yang Zou
7. Robert A. Vandermeulen
8. Jihun Yi
9. Vikash Sehwag
10. Lei Han

Papers from Nico Görnitz's perspective

Ordered by how strongly the researchers Nico Görnitz 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. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Toward Supervised Anomaly Detection (2014) — 418 citations
• SVM2Motif—Reconstructing Overlapping DNA Sequence Motifs by Mimicking an SVM Predictor (2015) — 9 citations
• ML2Motif—Reliable extraction of discriminative sequence motifs from learning machines (2017) — 8 citations
• Deep Semi-Supervised Anomaly Detection (2019) — 704 citations
• Deep One-Class Classification (2018) — 2,692 citations
• Feature Importance Measure for Non-linear Learning Algorithms (2016) — 40 citations
• Hierarchical Multitask Structured Output Learning for Large-scale Sequence Segmentation (2011) — 49 citations