Philippe Esling
Signal Processing · 2012–2023
A researcher in the Science corpus: 5 papers; trusts 94 researchers and is trusted by 10 researchers here, as inferred from the citation graph.
Subfields
- Signal Processing — 72 citations into it; #2,508 of 7,572 by trust here, #3,784 by citations
- Artificial Intelligence — 51 citations into it; #14,031 of 35,345 by trust here, #23,731 by citations
- Computer Vision and Pattern Recognition — 39 citations into it; #20,218 of 21,575 by trust here, #19,766 by citations
- Generative Models — 35 citations into it; #1,283 of 1,956 by trust here, #1,902 by citations
- Speech & Audio — 16 citations into it; #691 of 1,076 by trust here, #1,022 by citations
- Economics and Econometrics — 11 citations into it
- Probabilistic & Bayesian Methods — 9 citations into it
- Representation & Self-Supervised Learning — 9 citations into it
Whom Philippe Esling trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Eamonn J. Keogh
- Geoffrey E. Hinton
- Yoshua Bengio
- Radford M. Neal
- Jimmy Ba
- Chotirat (Ann) Ratanamahatana
- Danilo Jimenez Rezende
- Max Welling
- Aäron van den Oord
Who trusts Philippe Esling overall
- John Cristian Borges Gamboa
- Antoine Caillon
- Gowtham Atluri
- Jesse Engel
- Anuj Karpatne
- Alexandre D'efossez
- Vipin Kumar
- Lamtharn Hantrakul
Papers from Philippe Esling's perspective
Ordered by how strongly the researchers Philippe Esling 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)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- GENERATIVE ADVERSARIAL NETS
Their papers
- SingSong: Generating musical accompaniments from singing (2023) — 79 citations
- RAVE: A variational autoencoder for fast and high-quality neural audio synthesis (2021) — 181 citations
- Universal audio synthesizer control with normalizing flows (2019) — 46 citations
- Generative timbre spaces: regularizing variational auto-encoders with perceptual metrics (2018) — 42 citations
- Time-series data mining (2012) — 604 citations