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 …

The use of kernel functions is a common technique to extract important features from
datasets. A quantum computer can be used to estimate kernel entries as transition …

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 …

We introduce kernels with random Fourier features in the meta-learning framework for few-
shot learning. We propose meta variational random features (MetaVRF) to learn adaptive …

Retinopathy of prematurity (ROP) is a vasoproliferative retinal disease that affects premature
infants and causes permanent blindness if left untreated. Automated retinal diagnosis from …

In this work we introduce KERNELIZED TRANSFORMER, a generic, scalable, data driven
framework for learning the kernel function in Transformers. Our framework approximates the …

Additive Kernel SVM has been extensively used in many applications, including human
activity detection and pedestrian detection. Since training an additive kernel SVM model is …

We study the dynamics of a neural network in function space when optimizing the mean
squared error via gradient flow. We show that in the underparameterized regime the network …

Quantum machine learning (QML) is the spearhead of quantum computer applications. In
particular, quantum neural networks (QNN) are actively studied as the method that works …

Few-shot learning deals with the fundamental and challenging problem of learning from a
few annotated samples, while being able to generalize well on new tasks. The crux of few …