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.

Ex Input:
We present a graph neural network assisted Monte Carlo Tree Search approach for the classical traveling salesman problem (TSP). We adopt a greedy algorithm framework to construct the optimal solution to TSP by adding the nodes successively. A graph neural network (GNN) is trained to capture the local and global graph structure and give the prior probability of selecting each vertex every step. The prior probability provides a heuristics for MCTS, and the MCTS output is an improved probability for selecting the successive vertex, as it is the feedback information by fusing the prior with the scouting procedure. Experimental results on TSP up to 100 nodes demonstrate that the proposed method obtains shorter tours than other learning-based methods.

Ex Output:
A Graph Neural Network Assisted Monte Carlo Tree Search Approach to Traveling Salesman Problem


Ex Input:
We present a new unsupervised method for learning general-purpose sentence embeddings.
 Unlike existing methods which rely on local contexts, such as words
 inside the sentence or immediately neighboring sentences, our method selects, for
 each target sentence, influential sentences in the entire document based on a document
 structure. We identify a dependency structure of sentences using metadata
 or text styles. Furthermore, we propose a novel out-of-vocabulary word handling
 technique to model many domain-specific terms, which were mostly discarded by
 existing sentence embedding methods. We validate our model on several tasks
 showing 30% precision improvement in coreference resolution in a technical domain,
 and 7.5% accuracy increase in paraphrase detection compared to baselines.

Ex Output:
To train a sentence embedding using technical documents, our approach considers document structure to find broader context and handle out-of-vocabulary words.


Ex Input:
In distributed training, the communication cost due to the transmission of gradients
 or the parameters of the deep model is a major bottleneck in scaling up the number
 of processing nodes. To address this issue, we propose dithered quantization for
 the transmission of the stochastic gradients and show that training with Dithered
 Quantized Stochastic Gradients (DQSG) is similar to the training with unquantized
 SGs perturbed by an independent bounded uniform noise, in contrast to the other
 quantization methods where the perturbation depends on the gradients and hence,
 complicating the convergence analysis. We study the convergence of training
 algorithms using DQSG and the trade off between the number of quantization
 levels and the training time. Next, we observe that there is a correlation among the
 SGs computed by workers that can be utilized to further reduce the communication
 overhead without any performance loss. Hence, we develop a simple yet effective
 quantization scheme, nested dithered quantized SG (NDQSG), that can reduce the
 communication significantly without requiring the workers communicating extra
 information to each other. We prove that although NDQSG requires significantly
 less bits, it can achieve the same quantization variance bound as DQSG. Our
 simulation results confirm the effectiveness of training using DQSG and NDQSG
 in reducing the communication bits or the convergence time compared to the
 existing methods without sacrificing the accuracy of the trained model.

Ex Output:

The paper proposes and analyzes two quantization schemes for communicating Stochastic Gradients in distributed learning which would reduce communication costs compare to the state of the art while maintaining the same accuracy.