Hypernetworks, or hypernets for short, are neural networks that generate weights for another
neural network, known as the target network. They have emerged as a powerful deep …

Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …

Over the last decade, neural networks have reached almost every field of science and
become a crucial part of various real world applications. Due to the increasing spread …

Neural networks (NNs) are currently changing the computational paradigm on how to
combine data with mathematical laws in physics and engineering in a profound way …

In the past decade, advances in deep learning have resulted in breakthroughs in a variety of
areas, including computer vision, natural language understanding, speech recognition, and …

The factual knowledge acquired during pre-training and stored in the parameters of
Language Models (LMs) can be useful in downstream tasks (eg, question answering or …

Hypergraphs extend traditional graphs by allowing edges to connect multiple nodes, while
superhypergraphs further generalize this concept to represent even more complex …

Gradient-based meta-learning techniques are both widely applicable and proficient at
solving challenging few-shot learning and fast adaptation problems. However, they have …

This work studies the design of neural networks that can process the weights or gradients of
other neural networks, which we refer to as neural functional networks (NFNs). Despite a …

Deep learning describes a class of machine learning algorithms that are capable of
combining raw inputs into layers of intermediate features. These algorithms have recently …