The negative impact of label noise is well studied in classical supervis...
Recent trends in deep learning (DL) imposed hardware accelerators as the...
Since its debut in 2016, Federated Learning (FL) has been tied to the in...
Decentralised Machine Learning (DML) enables collaborative machine learn...
Tabular data synthesis is an emerging approach to circumvent strict
regu...
Synthetic tabular data emerges as an alternative for sharing knowledge w...
While data sharing is crucial for knowledge development, privacy concern...
Generative Adversarial Networks (GANs) are typically trained to synthesi...
Multi-label learning is an emerging extension of the multi-class
classif...
Tabular generative adversarial networks (TGAN) have recently emerged to ...
Classification algorithms have been widely adopted to detect anomalies f...
While data sharing is crucial for knowledge development, privacy concern...
Labeling real-world datasets is time consuming but indispensable for
sup...
DNN learning jobs are common in today's clusters due to the advances in ...
Robustness to label noise is a critical property for weakly-supervised
c...
Noisy labeled data is more a norm than a rarity for self-generated conte...
Classification algorithms have been widely adopted to detect anomalies f...
Today's big data clusters based on the MapReduce paradigm are capable of...
Variational Auto-encoders (VAEs) have been very successful as methods fo...