Abstract Randomized Neural Networks explore the behavior of neural systems where the
majority of connections are fixed, either in a stochastic or a deterministic fashion. Typical …

Whether it is used for predictive maintenance, intrusion detection or surveillance, on-device
anomaly detection is a very valuable functionality in sensor and Internet-of-things (IoT) …

Bayesian optimization usually assumes that a Bayesian prior is given. However, the strong
theoretical guarantees in Bayesian optimization are often regrettably compromised in …

Several recently proposed architectures of neural networks such as ResNeXt, Inception,
Xception, SqueezeNet and Wide ResNet are based on the designing idea of having multiple …

The inductive bias of a neural network is largely determined by the architecture and the
training algorithm. To achieve good generalization, how to effectively train a neural network …

Inspired by the Thomson problem in physics where the distribution of multiple propelling
electrons on a unit sphere can be modeled via minimizing some potential energy …

For nonconvex optimization in machine learning, this article proves that every local minimum
achieves the globally optimal value of the perturbable gradient basis model at any …

This paper proposes deep kernel learning networks with multiple learning paths (DKL-MLP)
for nonlinear function approximation. Leveraging the random feature (RF) map** …

Several recently proposed architectures of neural networks such as ResNeXt, Inception,
Xception, SqueezeNet and Wide ResNet are based on the designing idea of having multiple …

Diffusion probabilistic models have been successfully used to generate data from noise.
However, most diffusion models are computationally expensive and difficult to interpret with …