Xavier Favory

Signal Processing · 2017–2020

A researcher in the Science corpus: 7 papers; trusts 182 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Signal Processing — 142 citations into it; #2,190 of 7,572 by trust here, #1,629 by citations
• Computer Vision and Pattern Recognition — 109 citations into it; #12,882 of 21,575 by trust here, #11,721 by citations
• Artificial Intelligence — 72 citations into it; #34,604 of 35,345 by trust here, #33,294 by citations
• Evaluation & Benchmarks — 33 citations into it; #1,573 of 3,147 by trust here, #1,638 by citations
• Representation & Self-Supervised Learning — 17 citations into it
• Developmental Biology — 16 citations into it; #668 of 711 by trust here, #672 by citations
• Music — 12 citations into it; #142 of 281 by trust here, #67 by citations
• Speech & Audio — 11 citations into it; #541 of 1,076 by trust here, #491 by citations

Whom Xavier Favory trusts overall

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

1. Eduardo Fonseca
2. Justin Salamon
3. Frederic Font
4. Alex Krizhevsky
5. Geoffrey E. Hinton
6. Diederik P. Kingma
7. Karen Simonyan
8. D. Ellis
9. Kaiming He
10. Yoshua Bengio

Who trusts Xavier Favory overall

1. Eduardo Fonseca
2. Xavier Serra
3. Frederic Font
4. Jordi Pons
5. Jakob Abeßer
6. Manoj Plakal
7. Benjamin Elizalde
8. Scott Wisdom
9. D. Ellis
10. Nicolas Turpault

Papers from Xavier Favory's perspective

Ordered by how strongly the researchers Xavier Favory trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet: A large-scale hierarchical image database
5. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. Gradient-based learning applied to document recognition
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• COALA: Co-Aligned Autoencoders for Learning Semantically Enriched Audio Representations (2020) — 35 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
• 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