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: This paper is an empirical study of the distributed deep learning for a question answering subtask: answer selection. Comparison studies of SGD, MSGD, DOWNPOUR and EASGD/EAMSGD algorithms have been presented. Experimental results show that the message passing interface based distributed framework can accelerate the convergence speed at a sublinear scale. This paper demonstrates the importance of distributed training: with 120 workers, an 83x speedup is achievable and running time is decreased from 107.9 hours to 1.3 hours, which will benefit the productivity significantly.

Output: Distributed Deep Learning for Answer Selection


Input: Consider Input: Semantic segmentation tasks can be well modeled by Markov Random Field (MRF). This paper addresses semantic segmentation by incorporating high-order relations and mixture of label contexts into MRF. Unlike previous works that optimized MRFs using iterative algorithm, we solve MRF by proposing a Convolutional Neural Network (CNN), namely Deep Parsing Network (DPN), which enables deterministic end-to-end computation in a single forward pass. Specifically, DPN extends a contemporary CNN to model unary terms and additional layers are devised to approximate the mean field (MF) algorithm for pairwise terms. It has several appealing properties. First, different from the recent works that required many iterations of MF during back-propagation, DPN is able to achieve high performance by approximating one iteration of MF. Second, DPN represents various types of pairwise terms, making many existing models as its special cases. Furthermore, pairwise terms in DPN provide a unified framework to encode rich contextual information in high-dimensional data, such as images and videos. Third, DPN makes MF easier to be parallelized and speeded up, thus enabling efficient inference. DPN is thoroughly evaluated on standard semantic image/video segmentation benchmarks, where a single DPN model yields state-of-the-art segmentation accuracies on PASCAL VOC 2012, Cityscapes dataset and CamVid dataset.

Output: Deep Learning Markov Random Field for Semantic Segmentation


Input: Consider Input: <lb>Nonlinear component analysis such as kernel Principle Component Analysis (KPCA) and kernel<lb>Canonical Correlation Analysis (KCCA) are widely used in machine learning, statistics and data analysis,<lb>but they can not scale up to big datasets. Recent attempts have employed random feature approximations<lb>to convert the problem to the primal form for linear computational complexity. However, to obtain high<lb>quality solutions, the number of random features should be the same order of magnitude as the number<lb>of data points, making such approach not directly applicable to the regime with millions of data points.<lb>We propose a simple, computationally efficient, and memory friendly algorithm based on the “doubly<lb>stochastic gradients” to scale up a range of kernel nonlinear component analysis, such as kernel PCA, CCA<lb>and SVD. Despite the non-convex nature of these problems, our method enjoys theoretical guarantees<lb>that it converges at the rate Õ(1/t) to the global optimum, even for the top k eigen subspace. Unlike<lb>many alternatives, our algorithm does not require explicit orthogonalization, which is infeasible on big<lb>datasets. We demonstrate the effectiveness and scalability of our algorithm on large scale synthetic and<lb>real world datasets.


Output: Scale Up Nonlinear Component Analysis with Doubly Stochastic Gradients