Sac2Vec
Network representation learning (also known as information network embedding) has been the central piece of research in social and information network analytics for the last couple of years. An information network can be viewed as a linked structure of a set of entities. A set of linked web pages and documents, a set of users in a social network are common examples of information network. Typically a node in the information network is formed with a unique id, some content information and the links to its direct neighbors. Information network representation techniques traditionally use only link structure of the network. But the textual or other types of content in each node plays an important role to understand the underlying semantics of the network. In this paper, we propose Sac2Vec, a network representation technique using structure and content. It is a multi-layered graph approach which uses a random walk to generate the node embedding. Our approach is simple and computationally fast, yet able to use the content as a complement to structure and the vice-versa. Experimental evaluations on three real world publicly available datasets show the merit of our approach compared to state-of-the-art algorithms in the domain. …

Deep Convolutional Cascade for Face Alignment (DeCaFA)
Face Alignment is an active computer vision domain, that consists in localizing a number of facial landmarks that vary across datasets. State-of-the-art face alignment methods either consist in end-to-end regression, or in refining the shape in a cascaded manner, starting from an initial guess. In this paper, we introduce DeCaFA, an end-to-end deep convolutional cascade architecture for face alignment. DeCaFA uses fully-convolutional stages to keep full spatial resolution throughout the cascade. Between each cascade stage, DeCaFA uses multiple chained transfer layers with spatial softmax to produce landmark-wise attention maps for each of several landmark alignment tasks. Weighted intermediate supervision, as well as efficient feature fusion between the stages allow to learn to progressively refine the attention maps in an end-to-end manner. We show experimentally that DeCaFA significantly outperforms existing approaches on 300W, CelebA and WFLW databases. In addition, we show that DeCaFA can learn fine alignment with reasonable accuracy from very few images using coarsely annotated data. …

Apriori Algorithm
Apriori is an algorithm for frequent item set mining and association rule learning over transactional databases. It proceeds by identifying the frequent individual items in the database and extending them to larger and larger item sets as long as those item sets appear sufficiently often in the database. The frequent item sets determined by Apriori can be used to determine association rules which highlight general trends in the database: this has applications in domains such as market basket analysis. …

MiMatrix
In this paper, we present a co-designed petascale high-density GPU cluster to expedite distributed deep learning training with synchronous Stochastic Gradient Descent~(SSGD). This architecture of our heterogeneous cluster is inspired by Harvard architecture. Regarding to different roles in the system, nodes are configured as different specifications. Based on the topology of the whole system’s network and properties of different types of nodes, we develop and implement a novel job server parallel software framework, named by ‘\textit{MiMatrix}’, for distributed deep learning training. Compared to the parameter server framework, in which parameter server is a bottleneck of data transfer in AllReduce algorithm of SSGD, the job server undertakes all of controlling, scheduling and monitoring tasks without model data transfer. In MiMatrix, we propose a novel GPUDirect Remote direct memory access~(RDMA)-aware parallel algorithm of AllReucde executed by computing servers, which both computation and handshake message are $O(1)$ at each epoch …