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: This paper presents the ballistic graph neural network. Ballistic graph neural network tackles the weight distribution from a transportation perspective and has many different properties comparing to the traditional graph neural network pipeline. The ballistic graph neural network does not require to calculate any eigenvalue. The filters propagate exponentially faster($\sigma^2 \sim T^2$) comparing to traditional graph neural network($\sigma^2 \sim T$). We use a perturbed coin operator to perturb and optimize the diffusion rate. Our results show that by selecting the diffusion speed, the network can reach a similar accuracy with fewer parameters. We also show the perturbed filters act as better representations comparing to pure ballistic ones. We provide a new perspective of training graph neural network, by adjusting the diffusion rate, the neural network's performance can be improved.

Answer: A new perspective on how to collect the correlation between nodes based on diffusion properties.


Question: Machine translation has recently achieved impressive performance thanks to recent advances in deep learning and the availability of large-scale parallel corpora. There have been numerous attempts to extend these successes to low-resource language pairs, yet requiring tens of thousands of parallel sentences. In this work, we take this research direction to the extreme and investigate whether it is possible to learn to translate even without any parallel data. We propose a model that takes sentences from monolingual corpora in two different languages and maps them into the same latent space. By learning to reconstruct in both languages from this shared feature space, the model effectively learns to translate without using any labeled data. We demonstrate our model on two widely used datasets and two language pairs, reporting BLEU scores of 32.8 and 15.1 on the Multi30k and WMT English-French datasets, without using even a single parallel sentence at training time.

Answer: We propose a new unsupervised machine translation model that can learn without using parallel corpora; experimental results show impressive performance on multiple corpora and pairs of languages.


Question: Large Transformer models routinely achieve state-of-the-art results on
 a number of tasks but training these models can be prohibitively costly,
 especially on long sequences. We introduce two techniques to improve
 the efficiency of Transformers. For one, we replace dot-product attention
 by one that uses locality-sensitive hashing, changing its complexity
 from O(L^2) to O(L), where L is the length of the sequence.
 Furthermore, we use reversible residual layers instead of the standard
 residuals, which allows storing activations only once in the training
 process instead of N times, where N is the number of layers.
 The resulting model, the Reformer, performs on par with Transformer models
 while being much more memory-efficient and much faster on long sequences.

Answer:
Efficient Transformer with locality-sensitive hashing and reversible layers