Accurate remaining useful life (RUL) prediction for rolling bearings encounters many
challenges such as complex degradation processes, varying working conditions, and …

Deep learning is a class of machine learning which performs much better on unstructured
data. Deep learning techniques are outperforming current machine learning techniques. It …

Due to the modality gap between visible and infrared images with high visual ambiguity,
learning diverse modality-shared semantic concepts for visible-infrared person re …

Weakly supervised semantic segmentation produces pixel-level localization from class
labels; however, a classifier trained on such labels is likely to focus on a small discriminative …

Despite their renowned in-domain predictive power, convolutional neural networks are
known to rely more on high-frequency patterns that humans deem superficial than on low …

Q-learning is arguably one of the most applied representative reinforcement learning
approaches and one of the off-policy strategies. Since the emergence of Q-learning, many …

Deep neural networks excel at learning the training data, but often provide incorrect and
confident predictions when evaluated on slightly different test examples. This includes …

We decompose the evidence lower bound to show the existence of a term measuring the
total correlation between latent variables. We use this to motivate the beta-TCVAE (Total …

We define and address the problem of unsupervised learning of disentangled
representations on data generated from independent factors of variation. We propose …

Learning useful representations with little or no supervision is a key challenge in artificial
intelligence. We provide an in-depth review of recent advances in representation learning …