Recent studies show that a reproducing kernel Hilbert space (RKHS) is not a suitable space
to model functions by neural networks as the curse of dimensionality (CoD) cannot be …

Vision transformers using self-attention or its proposed alternatives have demonstrated
promising results in many image related tasks. However, the underpinning inductive bias of …

We introduce the CRONOS algorithm for convex optimization of two-layer neural networks.
CRONOS is the first algorithm capable of scaling to high-dimensional datasets such as …

We develop an analytical framework to characterize the set of optimal ReLU neural networks
by reformulating the non-convex training problem as a convex program. We show that the …

The nonconvexity of the artificial neural network (ANN) training landscape brings
optimization difficulties. While the traditional back-propagation stochastic gradient descent …

This paper introduces a novel theoretical framework for the analysis of vector-valued neural
networks through the development of vector-valued variation spaces, a new class of …

In this paper, we study the optimality gap between two-layer ReLU networks regularized with
weight decay and their convex relaxations. We show that when the training data is random …

We consider a binary classifier defined as the sign of a tropical rational function, that is, as
the difference of two convex piecewise linear functions. The parameter space of ReLU …

A novel fuzzy adaptive knowledge-based inference neural network (FAKINN) is proposed in
this study. Conventional fuzzy cluster-based neural networks (FCBNNs) suffer from the …

We introduce the class of SO-friendly neural networks, which include several models used in
practice including networks with 2 layers of hidden weights where the number of inputs is …