Many modern unsupervised or semi-supervised machine learning algorithms rely on
Bayesian probabilistic models. These models are usually intractable and thus require …

Tensor decomposition has been demonstrated to be successful in a wide range of
applications, from neuroscience and wireless communications to social networks. In an …

We propose a new regularization method based on virtual adversarial loss: a new measure
of local smoothness of the conditional label distribution given input. Virtual adversarial loss …

This paper develops variational continual learning (VCL), a simple but general framework
for continual learning that fuses online variational inference (VI) and recent advances in …

Catastrophic forgetting is a problem of neural networks that loses the information of the first
task after training the second task. Here, we propose a method, ie incremental moment …

In federated learning, a central server coordinates the training of a single model on a
massively distributed network of devices. This setting can be naturally extended to a multi …

This paper introduces the variational Rényi bound (VR) that extends traditional variational
inference to Rényi's alpha-divergences. This new family of variational methods unifies a …

Humans and animals can learn complex predictive models that allow them to accurately and
reliably reason about real-world phenomena, and they can adapt such models extremely …

The use of Bayesian methods in large-scale data settings is attractive because of the rich
hierarchical models, uncertainty quantification, and prior specification they provide …

Detecting code clones has many software engineering applications. Existing approaches
either do not scale to large code bases or are not robust against minor code modifications. In …