The increasing computational requirements of deep neural networks (DNNs) have led to
significant interest in obtaining DNN models that are sparse, yet accurate. Recent work has …

The energy landscape of high-dimensional non-convex optimization problems is crucial to
understanding the effectiveness of modern deep neural network architectures. Recent works …

Deep model fusion/merging is an emerging technique that merges the parameters or
predictions of multiple deep learning models into a single one. It combines the abilities of …

We develop a mathematically rigorous framework for multilayer neural networks in the mean
field regime. As the network's widths increase, the network's learning trajectory is shown to …

The performance of variational quantum algorithms relies on the success of using quantum
and classical computing resources in tandem. Here, we study how these quantum and …

We systematize the approach to the investigation of deep neural network landscapes by
basing it on the geometry of the space of implemented functions rather than the space of …

Viewing neural network models in terms of their loss landscapes has a long history in the
statistical mechanics approach to learning, and in recent years it has received attention …

Classical algorithms are often not effective for solving nonconvex optimization problems
where local minima are separated by high barriers. In this paper, we explore possible …

Deep neural networks (DNNs) defy the classical bias-variance trade-off: adding parameters
to a DNN that interpolates its training data will typically improve its generalization …

Linear interpolation between initial neural network parameters and converged parameters
after training with stochastic gradient descent (SGD) typically leads to a monotonic decrease …