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.
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Question: Recent work has demonstrated that neural networks are vulnerable to adversarial examples, i.e., inputs that are almost indistinguishable from natural data and yet classified incorrectly by the network. To address this problem, we study the adversarial robustness of neural networks through the lens of robust optimization. This approach provides us with a broad and unifying view on much prior work on this topic. Its principled nature also enables us to identify methods for both training and attacking neural networks that are reliable and, in a certain sense, universal. In particular, they specify a concrete security guarantee that would protect against a well-defined class of adversaries. These methods let us train networks with significantly improved resistance to a wide range of adversarial attacks. They also suggest robustness against a first-order adversary as a natural security guarantee. We believe that robustness against such well-defined classes of adversaries is an important stepping stone towards fully resistant deep learning models.

Answer: We provide a principled, optimization-based re-look at the notion of adversarial examples, and develop methods that produce models that are adversarially robust against a wide range of adversaries.


Question: 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.

Answer: NovoGrad -  an adaptive SGD method with layer-wise gradient normalization and decoupled weight decay. 


Question: Deep learning models have outperformed traditional methods in many fields such
 as natural language processing and computer vision. However, despite their
 tremendous success, the methods of designing optimal Convolutional Neural Networks
 (CNNs) are still based on heuristics or grid search. The resulting networks
 obtained using these techniques are often overparametrized with huge computational
 and memory requirements. This paper focuses on a structured, explainable
 approach towards optimal model design that maximizes accuracy while keeping
 computational costs tractable. We propose a single-shot analysis of a trained CNN
 that uses Principal Component Analysis (PCA) to determine the number of filters
 that are doing significant transformations per layer, without the need for retraining.
 It can be interpreted as identifying the dimensionality of the hypothesis space
 under consideration. The proposed technique also helps estimate an optimal number
 of layers by looking at the expansion of dimensions as the model gets deeper.
 This analysis can be used to design an optimal structure of a given network on
 a dataset, or help to adapt a predesigned network on a new dataset. We demonstrate
 these techniques by optimizing VGG and AlexNet networks on CIFAR-10,
 CIFAR-100 and ImageNet datasets.

Answer:
We present a single shot analysis of a trained neural network to remove redundancy and identify optimal network structure