Bob L. Sturm

Computer Vision and Pattern Recognition · 2014–2017

A researcher in the Science corpus: 3 papers; trusts 40 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 17 citations into it; #7,880 of 21,575 by trust here, #12,992 by citations
• Artificial Intelligence — 15 citations into it; #20,350 of 35,345 by trust here, #25,809 by citations
• Signal Processing — 12 citations into it; #2,175 of 7,572 by trust here, #3,428 by citations
• Safety, Robustness & Interpretability — 9 citations into it
• Model Architectures — 7 citations into it
• Cognitive Neuroscience — 5 citations into it
• Natural Language Processing — 5 citations into it; #3,452 of 5,688 by trust here, #4,682 by citations

Whom Bob L. Sturm trusts overall

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

1. Tao Li
2. Matthias Mauch
3. Geoffrey E. Hinton
4. Eric J. Humphrey
5. Yann LeCun
6. Sepp Hochreiter
7. Jean-Julien Aucouturier
8. Yoshua Bengio
9. Alex Krizhevsky
10. Ilya Sutskever

Who trusts Bob L. Sturm overall

1. Saumitra Mishra
2. Hao-Wen Dong
3. Nantheera Anantrasirichai
4. Wen-Yi Hsiao
5. Drew A. Hudson
6. Andrew Slavin Ross
7. Simon E. Dixon
8. Yi-Hsuan Yang
9. D. Bull
10. Li-Chia Yang

Papers from Bob L. Sturm's perspective

Ordered by how strongly the researchers Bob L. Sturm 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. Long Short-Term Memory
4. Gradient-based learning applied to document recognition
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• A Simple Method to Determine if a Music Information Retrieval System is a “Horse” (2014) — 160 citations
• Music transcription modelling and composition using deep learning (2016) — 190 citations
• Local Interpretable Model-Agnostic Explanations for Music Content Analysis (2017) — 204 citations