Abstract Model complexity is a fundamental problem in deep learning. In this paper, we
conduct a systematic overview of the latest studies on model complexity in deep learning …

We propose $\textit {Mish} $, a novel self-regularized non-monotonic activation function
which can be mathematically defined as: $ f (x)= x\tanh (softplus (x)) $. As activation …

In this paper, a deep collocation method (DCM) for thin plate bending problems is proposed.
This method takes advantage of computational graphs and backpropagation algorithms …

During the past five years the Bayesian deep learning community has developed
increasingly accurate and efficient approximate inference procedures that allow for …

The human population is growing at a very rapid scale. With this progressive growth, it is
extremely important to ensure that healthy food is available for the survival of the inhabitants …

We perform a careful, thorough, and large scale empirical study of the correspondence
between wide neural networks and kernel methods. By doing so, we resolve a variety of …

Neural Tangents is a library designed to enable research into infinite-width neural networks.
It provides a high-level API for specifying complex and hierarchical neural network …

In recent years, state-of-the-art methods in computer vision have utilized increasingly deep
convolutional neural network architectures (CNNs), with some of the most successful models …

We prove that a randomly initialized neural network of* any architecture* has its Tangent
Kernel (NTK) converge to a deterministic limit, as the network widths tend to infinity. We …

Quantum architecture Search (QAS) is a promising direction for optimization and automated
design of quantum circuits towards quantum advantage. Recent techniques in QAS …