Neural networks, as powerful tools for data mining and knowledge engineering, can learn
from data to build feature‐based classifiers and nonlinear predictive models. Training neural …

The class of random features is one of the most popular techniques to speed up kernel
methods in large-scale problems. Related works have been recognized by the NeurIPS Test …

Unsupervised deep hash functions have not shown satisfactory improvements against the
shallow alternatives, and usually, require supervised pretraining to avoid getting stuck in …

This paper introduces kernel continual learning, a simple but effective variant of continual
learning that leverages the non-parametric nature of kernel methods to tackle catastrophic …

Face alignment has been extensively studied in computer vision community due to its
fundamental role in facial analysis, but it remains an unsolved problem. The major …

We propose non-stationary spectral kernels for Gaussian process regression by modelling
the spectral density of a non-stationary kernel function as a mixture of input-dependent …

There are applications that may require removing the trace of a sample from the system, eg,
a user requests their data to be deleted, or corrupted data is discovered. Simply removing a …

Learning parameters from voluminous data can be prohibitive in terms of memory and
computational requirements. We propose a 'compressive learning'framework, where we …

In recent years, self-attention has become the dominant paradigm for sequence modeling in
a variety of domains. However, in domains with very long sequence lengths the $\mathcal …

Kernels are powerful and versatile tools in machine learning and statistics. Although the
notion of universal kernels and characteristic kernels has been studied, kernel selection still …