This article provides a comprehensive survey of bias mitigation methods for achieving
fairness in Machine Learning (ML) models. We collect a total of 341 publications concerning …

With the widespread use of artificial intelligence (AI) systems and applications in our
everyday lives, accounting for fairness has gained significant importance in designing and …

The recent release of large-scale healthcare datasets has greatly propelled the research of
data-driven deep learning models for healthcare applications. However, due to the nature of …

We consider the task of unsupervised extraction of meaningful latent representations of
speech by applying autoencoding neural networks to speech waveforms. The goal is to …

Embeddings are functions that map raw input data to low-dimensional vector
representations, while preserving important semantic information about the inputs. Pre …

Despite impressive performance as evaluated on iid holdout data, deep neural networks
depend heavily on superficial statistics of the training data and are liable to break under …

We consider the problem of domain generalization, namely, how to learn representations
given data from a set of domains that generalize to data from a previously unseen domain …

Recent work has shown deep learning can accelerate the prediction of physical dynamics
relative to numerical solvers. However, limited physical accuracy and an inability to …

Cross-modal hashing has garnered considerable attention and gained great success in
many cross-media similarity search applications due to its prominent computational …

How to effectively mitigate the bias of feedback in recommender systems is an important
research topic. In this paper, we first describe the generation process of the biased and …