Simon E. Dixon
Signal Processing · 2001–2019
A researcher in the Science corpus: 7 papers; trusts 101 researchers and is trusted by 39 researchers here, as inferred from the citation graph.
Subfields
- Signal Processing — 48 citations into it; #880 of 7,572 by trust here, #1,485 by citations
- Computer Vision and Pattern Recognition — 28 citations into it; #8,454 of 21,575 by trust here, #13,269 by citations
- Artificial Intelligence — 27 citations into it; #7,741 of 35,345 by trust here, #15,236 by citations
- Speech & Audio — 18 citations into it; #389 of 1,076 by trust here, #590 by citations
- Model Architectures — 10 citations into it
- Natural Language Processing — 10 citations into it
- Optimization & Training — 9 citations into it
- Safety, Robustness & Interpretability — 9 citations into it
Whom Simon E. Dixon trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yoshua Bengio
- Paris Smaragdis
- Masataka Goto
- Geoffrey E. Hinton
- Matthew D. Zeiler
- Yann LeCun
- Valentin Emiya
- Nicolas Boulanger-Lewandowski
- Alex Krizhevsky
- Karen Simonyan
Who trusts Simon E. Dixon overall
- Emmanouil Benetos
- D. Ellis
- Anssi Klapuri
- Hyeong-Seok Choi
- Yu-Siang Huang
- Jong Wook Kim
- Colin Raffel
- Justin Salamon
- Dimitrios Giannoulis
- Jordi Pons
Papers from Simon E. Dixon's perspective
Ordered by how strongly the researchers Simon E. Dixon trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- GENERATIVE ADVERSARIAL NETS
Their papers
- An End-to-End Neural Network for Polyphonic Piano Music Transcription (2015) — 302 citations
- Wave-U-Net: A Multi-Scale Neural Network for End-to-End Audio Source Separation (2018) — 709 citations
- Automatic music transcription: challenges and future directions (2013) — 328 citations
- PYIN: A fundamental frequency estimator using probabilistic threshold distributions (2014) — 455 citations
- Automatic Music Transcription: An Overview (2019) — 295 citations
- Local Interpretable Model-Agnostic Explanations for Music Content Analysis (2017) — 204 citations
- Automatic Extraction of Tempo and Beat From Expressive Performances (2001) — 435 citations