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.

Example Input: This work provides an additional step in the theoretical understanding of neural networks. We consider neural networks with one hidden layer and show that when learning symmetric functions, one can choose initial conditions so that standard SGD training efficiently produces generalization guarantees. We empirically verify this and show that this does not hold when the initial conditions are chosen at random. The proof of convergence investigates the interaction between the two layers of the network. Our results highlight the importance of using symmetry in the design of neural networks.
Example Output: When initialized properly, neural networks can learn the simple class of symmetric functions; when initialized randomly, they fail.

Example Input: Deep neural networks are known to be vulnerable to adversarial perturbations. In this paper, we bridge adversarial robustness of neural nets with Lyapunov stability of dynamical systems. From this viewpoint, training neural nets is equivalent to finding an optimal control of the discrete dynamical system, which allows one to utilize methods of successive approximations, an optimal control algorithm based on Pontryagin's maximum principle, to train neural nets. This decoupled training method allows us to add constraints to the optimization, which makes the deep model more robust. The constrained optimization problem can be formulated as a semi-definite programming problem and hence can be solved efficiently. Experiments show that our method effectively improves deep model's adversarial robustness.
Example Output: An adversarial defense method bridging robustness of deep neural nets with Lyapunov stability

Example Input: Transformers have achieved state-of-the-art results on a variety of natural language processing tasks.
Despite good performance, Transformers are still weak in long sentence modeling where the global attention map is too dispersed to capture valuable information.
In such case, the local/token features that are also significant to sequence modeling are omitted to some extent.
To address this problem, we propose a Multi-scale attention model (MUSE) by concatenating attention networks with convolutional networks and position-wise feed-forward networks to explicitly capture local and token features. Considering the parameter size and computation efficiency, we re-use the feed-forward layer in the original Transformer and adopt a lightweight dynamic convolution as implementation.
Experimental results show that the proposed model achieves substantial performance improvements over Transformer, especially on long sentences, and pushes the state-of-the-art from 35.6 to 36.2 on IWSLT 2014 German to English translation task, from 30.6 to 31.3 on IWSLT 2015 English to Vietnamese translation task. We also reach the state-of-art performance on WMT 2014 English to French translation dataset, with a BLEU score of 43.2.
Example Output:
This paper propose a new model which combines multi scale information for sequence to sequence learning.