In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

We propose NovoGrad, an adaptive stochastic gradient descent method with layer-wise gradient normalization and decoupled weight decay. In our experiments on neural networks for image classification, speech recognition, machine translation, and language modeling, it performs on par or better than well tuned SGD with momentum and Adam/AdamW. 
 Additionally, NovoGrad (1) is robust to the choice of learning rate and weight initialization, (2) works well in a large batch setting, and (3) has two times smaller memory footprint than Adam.
NovoGrad -  an adaptive SGD method with layer-wise gradient normalization and decoupled weight decay. 

Generative Adversarial Networks (GANs) can achieve state-of-the-art sample quality in generative modelling tasks but suffer from the mode collapse problem. Variational Autoencoders (VAE) on the other hand explicitly maximize a reconstruction-based data log-likelihood forcing it to cover all modes, but suffer from poorer sample quality. Recent works have proposed hybrid VAE-GAN frameworks which integrate a GAN-based synthetic likelihood to the VAE objective to address both the mode collapse and sample quality issues, with limited success. This is because the VAE objective forces a trade-off between the data log-likelihood and divergence to the latent prior. The synthetic likelihood ratio term also shows instability during training. We propose a novel objective with a ``"Best-of-Many-Samples" reconstruction cost and a stable direct estimate of the synthetic likelihood. This enables our hybrid VAE-GAN framework to achieve high data log-likelihood and low divergence to the latent prior at the same time and shows significant improvement over both hybrid VAE-GANS and plain GANs in mode coverage and quality.
We propose a new objective for training hybrid VAE-GANs which lead to significant improvement in mode coverage and quality.

We recently observed that convolutional filters initialized
 farthest apart from each other using offthe-
 shelf pre-computed Grassmannian subspace
 packing codebooks performed surprisingly well
 across many datasets. Through this short paper,
 we’d like to disseminate some initial results in this
 regard in the hope that we stimulate the curiosity
 of the deep-learning community towards considering
 classical Grassmannian subspace packing
 results as a source of new ideas for more efficient
 initialization strategies.

Initialize weights using off-the-shelf Grassmannian codebooks, get  faster training and better accuracy