In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

Ex Input:
We propose new methods to speed up convergence of the Alternating Direction Method of Multipliers (ADMM), a common optimization tool in the context of large scale and distributed learning. The proposed method accelerates the speed of convergence by automatically deciding the constraint penalty needed for parameter consensus in each iteration. In addition, we also propose an extension of the method that adaptively determines the maximum number of iterations to update the penalty. We show that this approach effectively leads to an adaptive, dynamic network topology underlying the distributed optimization. The utility of the new penalty update schemes is demonstrated on both synthetic and real data, including a computer vision application of distributed structure from motion.

Ex Output:
Fast ADMM Algorithm for Distributed Optimization with Adaptive Penalty

Ex Input:
We propose a new encoder-decoder approach to learn distributed sentence representations from unlabeled sentences. The word-to-vector representation is used, and convolutional neural networks are employed as sentence encoders, mapping an input sentence into a fixed-length vector. This representation is decoded using long short-term memory recurrent neural networks, considering several tasks, such as reconstructing the input sentence, or predicting the future sentence. We further describe a hierarchical encoder-decoder model to encode a sentence to predict multiple future sentences. By training our models on a large collection of novels, we obtain a highly generic convolutional sentence encoder that performs well in practice. Experimental results on several benchmark datasets, and across a broad range of applications, demonstrate the superiority of the proposed model over competing methods.

Ex Output:
Unsupervised Learning of Sentence Representations using Convolutional Neural Networks

Ex Input:
Automatic translation from natural language descriptions into programs is a long-<lb>standing challenging problem. In this work, we consider a simple yet impor-<lb>tant sub-problem: translation from textual descriptions to If-Then programs. We<lb>devise a novel neural network architecture for this task which we train end-to-<lb>end. Specifically, we introduce Latent Attention, which computes multiplicative<lb>weights for the words in the description in a two-stage process with the goal of<lb>better leveraging the natural language structures that indicate the relevant parts for<lb>predicting program elements. Our architecture reduces the error rate by 28.57%<lb>compared to prior art [3]. We also propose a one-shot learning scenario of If-Then<lb>program synthesis and simulate it with our existing dataset. We demonstrate a<lb>variation on the training procedure for this scenario that outperforms the original<lb>procedure, significantly closing the gap to the model trained with all data.

Ex Output:
Latent Attention For If-Then Program Synthesis