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.

Q: Recommender systems often use latent features to explain the behaviors of users and capture the properties of items. As users interact with different items over time, user and item features can influence each other, evolve and co-evolve over time. To accurately capture the fine grained nonlinear coevolution of these features, we propose a recurrent coevolutionary feature embedding process model, which combines recurrent neural network (RNN) with a multidimensional point process model. The RNN learns a nonlinear representation of user and item features which take into account mutual influence between user and item features, and the feature evolution over time. We also develop an efficient stochastic gradient algorithm for learning the model parameters, which can readily scale up to millions of events. Experiments on diverse real-world datasets demonstrate significant improvements in user behavior prediction compared to state-of-the-arts.

A: Recurrent Coevolutionary Feature Embedding Processes for Recommendation
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Q: The Region Connection Calculus (RCC) [41] is a well-known calculus for representing part-whole and topological relations. It plays an important role in qualitative spatial reasoning, geographical information science, and ontology. The computational complexity of reasoning with RCC5 and RCC8 (two fragments of RCC) as well as other qualitative spatial/temporal calculi has been investigated in depth in the literature. Most of these works focus on the consistency of qualitative constraint networks. In this paper, we consider the important problem of redundant qualitative constraints. For a set of qualitative constraints, we say a constraint (xRy) in is redundant if it is entailed by the rest of . A prime subnetwork of is a subset of which contains no redundant constraints and has the same solution set as . It is natural to ask how to compute such a prime subnetwork, and when it is unique. In this paper, we show that this problem is in general intractable, but becomes tractable if is over a tractable subalgebra S of a qualitative calculus. Furthermore, if S is a subalgebra of RCC5 or RCC8 in which weak composition distributes over nonempty intersections, then has a unique prime subnetwork, which can be obtained in cubic time by removing all redundant constraints simultaneously from . As a byproduct, we show that any path-consistent network over such a distributive subalgebra is weakly globally consistent and minimal. A thorough empirical analysis of the prime subnetwork upon real geographical data sets demonstrates the approach is able to identify significantly more redundant conCorresponding Author Email addresses: sanjiang.li@uts.edu.au (Sanjiang Li), zhiguo.long@student.uts.edu.au (Zhiguo Long), liuweiming@baidu.com (Weiming Liu), matt@duckham.org (Matt Duckham), aboth@student.unimelb.edu.au (Alan Both) Preprint submitted to Elsevier February 16, 2015 ar X iv :1 40 3. 06 13 v2 [ cs .A I] 1 3 Fe b 20 15 straints than previously proposed algorithms, especially in constraint networks with larger proportions of partial overlap relations.

A: On Redundant Topological Constraints
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Q: <lb>We study the problem of stochastic optimization for deep learning in the paral-<lb>lel computing environment under communication constraints. A new algorithm<lb>is proposed in this setting where the communication and coordination of work<lb>among concurrent processes (local workers), is based on an elastic force which<lb>links the parameters they compute with a center variable stored by the parameter<lb>server (master). The algorithm enables the local workers to perform more explo-<lb>ration, i.e. the algorithm allows the local variables to fluctuate further from the<lb>center variable by reducing the amount of communication between local workers<lb>and the master. We empirically demonstrate that in the deep learning setting, due<lb>to the existence of many local optima, allowing more exploration can lead to the<lb>improved performance. We propose synchronous and asynchronous variants of<lb>the new algorithm. We provide the stability analysis of the asynchronous vari-<lb>ant in the round-robin scheme and compare it with the more common parallelized<lb>method ADMM. We show that the stability of EASGD is guaranteed when a simple<lb>stability condition is satisfied, which is not the case for ADMM. We additionally<lb>propose the momentum-based version of our algorithm that can be applied in both<lb>synchronous and asynchronous settings. Asynchronous variant of the algorithm<lb>is applied to train convolutional neural networks for image classification on the<lb>CIFAR and ImageNet datasets. Experiments demonstrate that the new algorithm<lb>accelerates the training of deep architectures compared to DOWNPOUR and other<lb>common baseline approaches and furthermore is very communication efficient.

A:
Deep learning with Elastic Averaging SGD
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