Eduardo Fonseca
Signal Processing · 2017–2021
A researcher in the Science corpus: 17 papers; trusts 378 researchers and is trusted by 18 researchers here, as inferred from the citation graph.
Subfields
- Signal Processing — 210 citations into it; #1,119 of 7,572 by trust here, #853 by citations
- Computer Vision and Pattern Recognition — 164 citations into it; #8,452 of 21,575 by trust here, #8,502 by citations
- Artificial Intelligence — 151 citations into it; #14,958 of 35,345 by trust here, #19,018 by citations
- Evaluation & Benchmarks — 48 citations into it; #1,815 of 3,147 by trust here, #1,638 by citations
- Representation & Self-Supervised Learning — 38 citations into it
- Optimization & Training — 37 citations into it
- Speech & Audio — 36 citations into it; #624 of 1,076 by trust here, #491 by citations
- Model Architectures — 27 citations into it
Whom Eduardo Fonseca trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Sergey Ioffe
- Geoffrey E. Hinton
- Christian Szegedy
- Kaiming He
- Alex Krizhevsky
- Justin Salamon
- Jimmy Ba
- Yoshua Bengio
- Frederic Font
Who trusts Eduardo Fonseca overall
- Xavier Serra
- Frederic Font
- Xavier Favory
- Jordi Pons
- Manoj Plakal
- Andrés Ferraro
- D. Ellis
- Benjamin Elizalde
- Scott Wisdom
- Janek Ebbers
Papers from Eduardo Fonseca's perspective
Ordered by how strongly the researchers Eduardo Fonseca trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- The Benefit of Temporally-Strong Labels in Audio Event Classification (2021) — 159 citations
- Self-Supervised Learning from Automatically Separated Sound Scenes (2021) — 13 citations
- Improving Sound Event Classification by Increasing Shift Invariance in Convolutional Neural Networks (2021) — 11 citations
- Addressing Missing Labels in Large-Scale Sound Event Recognition Using a Teacher-Student Framework With Loss Masking (2020) — 27 citations
- What’s all the Fuss about Free Universal Sound Separation Data? (2020) — 96 citations
- Unsupervised Contrastive Learning of Sound Event Representations (2020) — 71 citations
- FSD50K: An Open Dataset of Human-Labeled Sound Events (2020) — 737 citations
- Improving Sound Event Detection in Domestic Environments using Sound Separation (2020) — 54 citations
- A Hybrid Parametric-Deep Learning Approach for Sound Event Localization and Detection (2019) — 6 citations
- Learning Sound Event Classifiers from Web Audio with Noisy Labels (2019) — 126 citations
- Audio tagging with noisy labels and minimal supervision (2019) — 103 citations
- Model-Agnostic Approaches To Handling Noisy Labels When Training Sound Event Classifiers (2019) — 10 citations
- General-purpose Tagging of Freesound Audio with AudioSet Labels: Task Description, Dataset, and Baseline (2018) — 167 citations
- Facilitating the Manual Annotation of Sounds When Using Large Taxonomies (2018) — 7 citations
- A Simple Fusion of Deep and Shallow Learning for Acoustic Scene Classification (2018) — 12 citations
- Freesound Datasets: A Platform for the Creation of Open Audio Datasets (2017) — 277 citations
- Acoustic Scene Classification by Ensembling Gradient Boosting Machine and Convolutional Neural Networks (2017) — 29 citations