See https://arxiv.org/abs/1709.09603 for details. [2GPUs] pyhon3 train.py --model=resnet --depth=40 --widen_factor=10 --optimizer=adamg --grassmann=True --learnRate=0 ...

ABSTRACT: Cardiovascular Diseases (CVDs) remain a leading cause of death in the United States. These diseases, including coronary heart disease, heart attack, and stroke, pose significant health risks ...

Abstract: The widespread use of Batch Normalization has enabled training deeper neural networks with more stable and faster results. However, the Batch Normalization works best using large batch size ...

Abstract: Batch Normalization is a widely used tool in neural networks to improve the generalization and convergence of training. However, on small datasets due to the difficulty of obtaining unbiased ...

Batch normalization (BatchNorm) is a widely adopted technique that enables faster and more stable training of deep neural networks. However, despite its pervasiveness, the exact reasons for ...

We aim to generate realistic images from text descriptions using GAN architecture. The network that we have designed is used for image generation for two datasets: MSCOCO and CUBS.