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.

Input: Consider Input: Recurrent neural networks scale poorly due to the intrinsic difficulty in parallelizing their state computations. For instance, the forward pass computation of ht is blocked until the entire computation of ht−1 finishes, which is a major bottleneck for parallel computing. In this work, we propose an alternative RNN implementation by deliberately simplifying the state computation and exposing more parallelism. The proposed recurrent unit operates as fast as a convolutional layer and 5-10x faster than cuDNN-optimized LSTM. We demonstrate the unit’s effectiveness across a wide range of applications including classification, question answering, language modeling, translation and speech recognition. We open source our implementation in PyTorch and CNTK1.

Output: Training RNNs as Fast as CNNs


Input: Consider Input: The basic features of some of the most versatile and popular open source frameworks for machine learning (TensorFlow, Deep Learning4j, and H2O) are considered and compared. Their comparative analysis was performed and conclusions were made as to the advantages and disadvantages of these platforms. The performance tests for the de facto standard MNIST data set were carried out on H2O framework for deep learning algorithms designed for CPU and GPU platforms for single-threaded and multithreaded modes of operation. Keywords—machine learning; deep learning; TensorFlow; Deep Learning4j; H2O; MNIST; multicore CPU; GPU.

Output: Comparative Analysis of Open Source Frameworks for Machine Learning with Use Case in Single- Threaded and Multi-Threaded Modes


Input: Consider Input: We propose multi-way, multilingual neural machine translation. The proposed approach enables a single neural translation model to translate between multiple languages, with a number of parameters that grows only linearly with the number of languages. This is made possible by having a single attention mechanism that is shared across all language pairs. We train the proposed multiway, multilingual model on ten language pairs from WMT’15 simultaneously and observe clear performance improvements over models trained on only one language pair. In particular, we observe that the proposed model significantly improves the translation quality of low-resource language pairs.


Output: Multi-Way, Multilingual Neural Machine Translation with a Shared Attention Mechanism