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Found 6 papers, 3 papers with code
Focal Modulation Networks for Interpretable Sound Classification
The increasing success of deep neural networks has raised concerns about their inherent black-box nature, posing challenges related to interpretability and trust.
Bayesian Deep Learning for Remaining Useful Life Estimation via Stein Variational Gradient Descent
In particular, we show through experimental studies on simulated run-to-failure turbofan engine degradation data that Bayesian deep learning models trained via Stein variational gradient descent consistently outperform with respect to convergence speed and predictive performance both the same models trained via parametric variational inference and their frequentist counterparts trained via backpropagation.
CL-MASR: A Continual Learning Benchmark for Multilingual ASR
To the best of our knowledge, CL-MASR is the first continual learning benchmark for the multilingual ASR task.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Resource-Efficient Separation Transformer
Transformers have recently achieved state-of-the-art performance in speech separation.
A Comparative Study between Bayesian and Frequentist Neural Networks for Remaining Useful Life Estimation in Condition-Based Maintenance
On the contrary, Bayesian deep learning has recently emerged as a promising solution to account for uncertainty in the training process, achieving state-of-the-art performance in many classification and regression tasks.
Deep Learning for 2D and 3D Rotatable Data: An Overview of Methods
Convolutional networks are successful due to their equivariance/invariance under translations.
