In recent years, deep learning (DL) has been widely studied using various methods across
the globe, especially with respect to training methods and network structures, proving highly …

Image diffusion models such as DALL-E 2, Imagen, and Stable Diffusion have attracted
significant attention due to their ability to generate high-quality synthetic images. In this work …

Diffusion models generate high-quality images but require dozens of forward passes. We
introduce Distribution Matching Distillation (DMD) a procedure to transform a diffusion model …

Neural architecture search (NAS) has attracted increasing attention. In recent years,
individual search methods have been replaced by weight-sharing search methods for higher …

Diffusion models have significantly advanced the fields of image, audio, and video
generation, but they depend on an iterative sampling process that causes slow generation …

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 recent explosive interest on transformers has suggested their potential to become
powerful``universal" models for computer vision tasks, such as classification, detection, and …

Symbolic regression is the process of identifying mathematical expressions that fit observed
output from a black-box process. It is a discrete optimization problem generally believed to …

In order to improve the accuracy of fault diagnosis, researchers are constantly trying to
develop new diagnostic models. However, limited by the inherent thinking of human beings …

In this paper, we propose a novel normalization method called gradient normalization (GN)
to tackle the training instability of Generative Adversarial Networks (GANs) caused by the …