Jordi Pons
Signal Processing · 2016–2022
A researcher in the Science corpus: 8 papers; trusts 254 researchers and is trusted by 44 researchers here, as inferred from the citation graph.
Subfields
- Signal Processing — 183 citations into it; #749 of 7,572 by trust here, #960 by citations
- Computer Vision and Pattern Recognition — 110 citations into it; #17,190 of 21,575 by trust here, #14,822 by citations
- Artificial Intelligence — 96 citations into it; #7,650 of 35,345 by trust here, #15,236 by citations
- Speech & Audio — 47 citations into it; #479 of 1,076 by trust here, #465 by citations
- Evaluation & Benchmarks — 28 citations into it; #1,568 of 3,147 by trust here, #1,638 by citations
- Generative Models — 28 citations into it
- Developmental Biology — 13 citations into it
- Computational Mechanics — 11 citations into it
Whom Jordi Pons trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Geoffrey E. Hinton
- Alex Krizhevsky
- Kaiming He
- Yoshua Bengio
- Aäron van den Oord
- Jimmy Ba
- Yann LeCun
- Ilya Sutskever
- Alex Graves
Who trusts Jordi Pons overall
- Eduardo Fonseca
- Alexandre D'efossez
- Lauri Juvela
- Joan Serrà
- Adam Polyak
- Xavier Serra
- Dario Rethage
- Zhifeng Kong
- Jinglin Liu
- Santiago Pascual
Papers from Jordi Pons's perspective
Ordered by how strongly the researchers Jordi Pons 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)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
Their papers
- End-to-end music source separation: is it possible in the waveform domain? (2018) — 78 citations
- A Wavenet for Speech Denoising (2017) — 489 citations
- Remixing music using source separation algorithms to improve the musical experience of cochlear implant users. (2016) — 63 citations
- Universal Speech Enhancement with Score-based Diffusion (2022) — 146 citations
- Upsampling Artifacts in Neural Audio Synthesis (2020) — 77 citations
- FSD50K: An Open Dataset of Human-Labeled Sound Events (2020) — 737 citations
- General-purpose Tagging of Freesound Audio with AudioSet Labels: Task Description, Dataset, and Baseline (2018) — 167 citations
- Freesound Datasets: A Platform for the Creation of Open Audio Datasets (2017) — 277 citations