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

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 …

Machine learning methods have achieved good performance and been widely applied in
various real-world applications. They can learn the model adaptively and be better fit for …

The structural design of functional molecules, also called molecular optimization, is an
essential chemical science and engineering task with important applications, such as drug …

In theory, importance sampling speeds up stochastic gradient algorithms for supervised
learning by prioritizing training examples. In practice, the cost of computing importances …

Highlights•Designed an end-to-end vision system for a mango picking robot.•Instance
segmentation and pick point detection are performed simultaneously.•It is robust to various …

Sampling methods (eg, node-wise, layer-wise, or subgraph) has become an indispensable
strategy to speed up training large-scale Graph Neural Networks (GNNs). However, existing …

We study the implicit regularization of mini-batch stochastic gradient descent, when applied
to the fundamental problem of least squares regression. We leverage a continuous-time …

It has been experimentally observed that distributed implementations of mini-batch
stochastic gradient descent (SGD) algorithms exhibit speedup saturation and decaying …

Randomized Numerical Linear Algebra (RandNLA) is an area which uses randomness,
most notably random sampling and random projection methods, to develop improved …