Recently, the speech community is seeing a significant trend of moving from deep neural
network based hybrid modeling to end-to-end (E2E) modeling for automatic speech …

Over the past decades, a tremendous amount of research has been done on the use of
machine learning for speech processing applications, especially speech recognition …

This paper presents XLSR which learns cross-lingual speech representations by pretraining
a single model from the raw waveform of speech in multiple languages. We build on …

Fine-tuning large pretrained models is an effective transfer mechanism in NLP. However, in
the presence of many downstream tasks, fine-tuning is parameter inefficient: an entire new …

In multi-task learning, multiple tasks are solved jointly, sharing inductive bias between them.
Multi-task learning is inherently a multi-objective problem because different tasks may …

As a new classification platform, deep learning has recently received increasing attention
from researchers and has been successfully applied to many domains. In some domains …

This paper deals with the use of emerging deep learning techniques in future wireless
communication networks. It will be shown that the data-driven approaches should not …

Domain adaptation has become a prominent problem setting in machine learning and
related fields. This review asks the question: How can a classifier learn from a source …

Numerous deep learning applications benefit from multi-task learning with multiple
regression and classification objectives. In this paper we make the observation that the …

Group recommendation has recently drawn a lot of attention to the recommender system
community. Currently, several deep learning-based approaches are leveraged to learn …