7 citations · 22 across the 11 of their papers we have counts for
7 papers · 1 filter
On tuning consistent annealed sampling for denoising score matching
Joan Serrà, Santiago Pascual, Jordi Pons
Score-based generative models provide state-of-the-art quality for image and audio synthesis. Sampling from these models is performed iteratively, typically employing a discretized…
Input complexity and out-of-distribution detection with likelihood-based generative models
Joan Serrà, David Álvarez, Vicenç Gómez +3
Likelihood-based generative models are a promising resource to detect out-of-distribution (OOD) inputs which could compromise the robustness or reliability of a machine learning sy…
Blow: a single-scale hyperconditioned flow for non-parallel raw-audio voice conversion
Joan Serrà, Santiago Pascual, Carlos Segura
End-to-end models for raw audio generation are a challenge, specially if they have to work with non-parallel data, which is a desirable setup in many situations. Voice conversion,…
Learning Problem-agnostic Speech Representations from Multiple Self-supervised Tasks
Santiago Pascual, Mirco Ravanelli, Joan Serrà +2
Learning good representations without supervision is still an open issue in machine learning, and is particularly challenging for speech signals, which are often characterized by l…
Towards a universal neural network encoder for time series
Joan Serrà, Santiago Pascual, Alexandros Karatzoglou
We study the use of a time series encoder to learn representations that are useful on data set types with which it has not been trained on. The encoder is formed of a convolutional…
Getting deep recommenders fit: Bloom embeddings for sparse binary input/output networks
Joan Serrà, Alexandros Karatzoglou
Recommendation algorithms that incorporate techniques from deep learning are becoming increasingly popular. Due to the structure of the data coming from recommendation domains (i.e…