Harald Steck

Information Systems · 2007–2019

A researcher in the Science corpus: 9 papers; trusts 135 researchers and is trusted by 79 researchers here, as inferred from the citation graph.

Subfields

• Information Systems — 107 citations into it; #139 of 8,771 by trust here, #1,142 by citations
• Recommender Systems — 52 citations into it
• Management Science and Operations Research — 51 citations into it; #364 of 11,946 by trust here, #1,836 by citations
• Artificial Intelligence — 46 citations into it; #3,535 of 35,345 by trust here, #11,624 by citations
• Marketing — 25 citations into it; #362 of 6,289 by trust here, #2,717 by citations
• Computer Vision and Pattern Recognition — 20 citations into it; #18,805 of 21,575 by trust here, #17,258 by citations
• Signal Processing — 18 citations into it
• Computer Science Applications — 14 citations into it

Whom Harald Steck trusts overall

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

1. Yehuda Koren
2. Arkadiusz Paterek
3. Yifan Hu
4. Ruslan Salakhutdinov
5. Markus Weimer
6. Geoffrey E. Hinton
7. Corinna Cortes
8. Thorsten Joachims
9. David J. Hand
10. Jerome H. Friedman

Who trusts Harald Steck overall

1. Charu C. Aggarwal
2. Bruno Pradel
3. Weiwei Guo
4. Jiawei Chen
5. Himanshu Jain
6. Tobias Schnabel
7. Daryl Lim
8. Simon Caton
9. Xiwang Yang
10. José Miguel Hernández-Lobato

Papers from Harald Steck's perspective

Ordered by how strongly the researchers Harald Steck 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
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. Going deeper with convolutions

Their papers

• Training and testing of recommender systems on data missing not at random (2010) — 425 citations
• Item popularity and recommendation accuracy (2011) — 334 citations
• Evaluation of recommendations: rating-prediction and ranking (2013) — 280 citations
• Gaussian Ranking by Matrix Factorization (2015) — 31 citations
• Hinge Rank Loss and the Area Under the ROC Curve (2007) — 35 citations
• Embarrassingly Shallow Autoencoders for Sparse Data (2019) — 331 citations
• Calibrated recommendations (2018) — 331 citations
• A survey of collaborative filtering based social recommender systems (2014) — 498 citations
• Circle-based recommendation in online social networks (2012) — 402 citations