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Found 5 papers, 1 papers with code
Propagating Variational Model Uncertainty for Bioacoustic Call Label Smoothing
We focus on using the predictive uncertainty signal calculated by Bayesian neural networks to guide learning in the self-same task the model is being trained on.
Dynamic Restrained Uncertainty Weighting Loss for Multitask Learning of Vocal Expression
We propose a novel Dynamic Restrained Uncertainty Weighting Loss to experimentally handle the problem of balancing the contributions of multiple tasks on the ICML ExVo 2022 Challenge.
Audio Self-supervised Learning: A Survey
Inspired by the humans' cognitive ability to generalise knowledge and skills, Self-Supervised Learning (SSL) targets at discovering general representations from large-scale data without requiring human annotations, which is an expensive and time consuming task.
Evaluating Deep Music Generation Methods Using Data Augmentation
We achieve this by measuring the change in predictive performance of a music mood/theme classifier after augmenting its training data with generated samples.
A deep matrix factorization method for learning attribute representations
Semi-Non-negative Matrix Factorization is a technique that learns a low-dimensional representation of a dataset that lends itself to a clustering interpretation.
