As technology advances and digital devices become prevalent, seamless
hu...
Pretraining neural networks with massive unlabeled datasets has become
p...
Out-of-distribution (OOD) detection is concerned with identifying data p...
Federated Learning (FL) is a distributed machine learning paradigm that
...
Self-Supervised Learning (SSL) is a new paradigm for learning discrimina...
This work introduces BRILLsson, a novel binary neural network-based
repr...
Deep neural networks have significantly improved performance on a range ...
Recently, Self-Supervised Representation Learning (SSRL) has attracted m...
What can neural networks learn about the visual world from a single imag...
Deep neural networks have become larger over the years with increasing d...
Federated Learning is a distributed machine learning paradigm dealing wi...
Predictive business process monitoring focuses on predicting future
char...
We investigate the capabilities of transfer learning in the area of
stru...
We introduce COLA, a self-supervised pre-training approach for learning ...
Learning general-purpose representations from multisensor data produced ...
Smartphones, wearables, and Internet of Things (IoT) devices produce a w...
Deep learning methods are successfully used in applications pertaining t...
Smart devices of everyday use (such as smartphones and wearables) are
in...
Stress can be seen as a physiological response to everyday emotional, me...