Stefan Steidl

Artificial Intelligence · 2009–2017

A researcher in the Science corpus: 10 papers; trusts 17 researchers and is trusted by 27 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 55 citations into it; #6,251 of 35,345 by trust here, #7,945 by citations
• Experimental and Cognitive Psychology — 52 citations into it; #968 of 9,731 by trust here, #2,259 by citations
• Signal Processing — 45 citations into it; #1,104 of 7,572 by trust here, #1,748 by citations
• Computer Vision and Pattern Recognition — 27 citations into it
• Representation & Self-Supervised Learning — 5 citations into it

Whom Stefan Steidl trusts overall

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

1. Björn Schuller
2. Florian Eyben
3. Martin Wöllmer
4. Chi-Chun Lee
5. Anton Batliner
6. Mark A. Hall
7. Felix Burkhardt
8. Ian H. Witten
9. Eibe Frank
10. Shrikanth S. Narayanan

Who trusts Stefan Steidl overall

1. Björn Schuller
2. Florian Eyben
3. Michel François Valstar
4. Anton Batliner
5. Hatice Gunes
6. Jun Deng
7. Carlos Busso
8. Duc Le
9. Emily Mower Provost
10. Christos-Nikolaos E. Anagnostopoulos

Papers from Stefan Steidl's perspective

Ordered by how strongly the researchers Stefan Steidl 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. ImageNet: A large-scale hierarchical image database
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Attention is All you Need (Neural Information Processing Systems)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Precision Learning: Towards Use of Known Operators in Neural Networks (2017) — 35 citations
• UI-Net: Interactive Artificial Neural Networks for Iterative Image Segmentation Based on a User Model (2017) — 37 citations
• The INTERSPEECH 2016 Computational Paralinguistics Challenge: Deception, Sincerity & Native Language (2016) — 350 citations
• A Survey on perceived speaker traits: Personality, likability, pathology, and the first challenge (2015) — 84 citations
• Computer Speech and Language: Introduction (2015) — 536 citations
• The INTERSPEECH 2013 computational paralinguistics challenge: social signals, conflict, emotion, autism (2013) — 805 citations
• Paralinguistics in speech and language - State-of-the-art and the challenge (2013) — 343 citations
• Recognising realistic emotions and affect in speech: State of the art and lessons learnt from the first challenge (2011) — 788 citations
• The INTERSPEECH 2010 paralinguistic challenge (2010) — 597 citations
• The INTERSPEECH 2009 emotion challenge (2009) — 1,032 citations