Emmanouil Benetos
Signal Processing · 2013–2023
A researcher in the Science corpus: 7 papers; trusts 39 researchers and is trusted by 9 researchers here, as inferred from the citation graph.
Subfields
- Signal Processing — 80 citations into it; #2,600 of 7,572 by trust here, #1,189 by citations
- Artificial Intelligence — 45 citations into it
- Computer Vision and Pattern Recognition — 42 citations into it; #9,276 of 21,575 by trust here, #7,750 by citations
- Speech & Audio — 20 citations into it
- Evaluation & Benchmarks — 10 citations into it
- Representation & Self-Supervised Learning — 10 citations into it
- Cognitive Neuroscience — 9 citations into it
- Model Architectures — 9 citations into it
Whom Emmanouil Benetos trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Paris Smaragdis
- Masataka Goto
- Valentin Emiya
- Yoshua Bengio
- Nicolas Boulanger-Lewandowski
- Simon E. Dixon
- Anssi Klapuri
- Geoffrey E. Hinton
- Yann LeCun
- Tuomas Virtanen
Who trusts Emmanouil Benetos overall
- Yu-Siang Huang
- Annamaria Mesaros
- Yi-Hsuan Yang
- Toni Heittola
- Bruno Korbar
- Curtis Hawthorne
- Eduardo Fonseca
- Sharath Adavanne
- Tuomas Virtanen
Papers from Emmanouil Benetos's perspective
Ordered by how strongly the researchers Emmanouil Benetos 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)
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Detection and Classification of Acoustic Scenes and Events (2015) — 748 citations
- An End-to-End Neural Network for Polyphonic Piano Music Transcription (2015) — 302 citations
- Automatic music transcription: challenges and future directions (2013) — 328 citations
- MERT: Acoustic Music Understanding Model with Large-Scale Self-supervised Training (2023) — 320 citations
- Automatic Music Transcription: An Overview (2019) — 295 citations
- Detection and Classification of Acoustic Scenes and Events: Outcome of the DCASE 2016 Challenge (2018) — 313 citations
- Sound event detection in synthetic audio: Analysis of the dcase 2016 task results (2017) — 23 citations