Frederic Font
Signal Processing · 2013–2020
A researcher in the Science corpus: 9 papers; trusts 152 researchers and is trusted by 19 researchers here, as inferred from the citation graph.
Subfields
- Signal Processing — 132 citations into it; #1,037 of 7,572 by trust here, #880 by citations
- Computer Vision and Pattern Recognition — 103 citations into it; #6,673 of 21,575 by trust here, #8,021 by citations
- Artificial Intelligence — 85 citations into it; #14,651 of 35,345 by trust here, #20,836 by citations
- Evaluation & Benchmarks — 34 citations into it; #1,687 of 3,147 by trust here, #1,638 by citations
- Optimization & Training — 19 citations into it
- Developmental Biology — 16 citations into it; #670 of 711 by trust here, #672 by citations
- Music — 14 citations into it; #157 of 281 by trust here, #76 by citations
- Model Architectures — 11 citations into it
Whom Frederic Font trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Eduardo Fonseca
- Diederik P. Kingma
- Alex Krizhevsky
- Geoffrey E. Hinton
- Christian Szegedy
- Yann LeCun
- Kaiming He
- Justin Salamon
- Yoshua Bengio
- Karol J. Piczak
Who trusts Frederic Font overall
- Eduardo Fonseca
- Xavier Favory
- Xavier Serra
- Karol J. Piczak
- Konstantinos Drossos
- Romain Serizel
- Nicolas Turpault
- Jordi Pons
- Felicia S. C. Lim
- Manoj Plakal
Papers from Frederic Font's perspective
Ordered by how strongly the researchers Frederic Font 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
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Freesound technical demo (2013) — 403 citations
- FSD50K: An Open Dataset of Human-Labeled Sound Events (2020) — 737 citations
- Search Result Clustering in Collaborative Sound Collections (2020) — 5 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
- Freesound Datasets: A Platform for the Creation of Open Audio Datasets (2017) — 277 citations