TASK DEFINITION: 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.
PROBLEM: Collaborative data consist of ratings relating two distinct sets of objects: users and items. Much of the work with such data focuses on filtering: predicting unknown ratings for pairs of users and items. In this paper we focus on the problem of visualizing the information. Given all of the ratings, our task is to embed all of the users and items as points in the same Euclidean space. We would like to place users near items that they have rated (or would rate) high, and far away from those they would give low ratings. We pose this problem as a real-valued non-linear Bayesian network and employ Markov chain Monte Carlo and expectation maximization to find an embedding. We present a metric by which to judge the quality of a visualization and compare our results to Eigentaste, locally linear embedding and cooccurrence data embedding on three real-world datasets.

SOLUTION: Visualization of Collaborative Data

PROBLEM: Fully connected network has been widely used in deep learning, and its computation efficiency is highly benefited from the matrix multiplication algorithm with cuBLAS on GPU. However, We found that, there exist some drawbacks of cuBLAS in calculating matrix A multiplies the transpose of matrix B (i.e., NT operation). To reduce the impact of NT operation by cuBLAS, we exploit the out-of-place transpose of matrix B to avoid using NT operation, and then we apply our method to Caffe, which is a popular deep learning tool. Our contribution is two-fold. First, we propose a naive method (TNN) and model-based method (MTNN) to increase the performance in calculating A × B , and it achieves about 4.7 times performance enhancement in our tested cases on GTX1080 card. Second, we integrate MTNN method into Caffe to enhance the efficiency in training fully connected networks, which achieves about 70% speedup compared to the original Caffe in our configured fully connected networks on GTX1080 card.

SOLUTION: Improving the Performance of Fully Connected Neural Networks by Out-of-Place Matrix Transpose

PROBLEM: In this paper, we introduce reactive multi-context systems (rMCSs), a framework for reactive reasoning in the presence of heterogeneous knowledge sources. In particular, we show how to integrate data streams into multi-context systems (MCSs) and how to model the dynamics of the systems, based on two types of bridge rules. We illustrate how several typical problems arising in the context of stream reasoning can be handled using our framework. Reasoning based on multiple knowledge sources that need to be integrated faces the problem of potential inconsistencies. We discuss various methods for handling inconsistencies, with a special focus on non-existence of equilibria. In particular, we show how methods developed for managed MCSs can be generalized to rMCSs. We also study the issue of nondeterminism in rMCSs. One way of avoiding nondeterminism is by applying an alternative, skeptical semantics. We show how such a semantics, called well-founded semantics, can be defined for rMCSs, and what the effect of using this semantics instead of the original one is. We investigate the complexity of various reasoning problems related to rMCSs. Finally, we discuss related work, with a special focus on two of the most relevant approaches w.r.t. stream reasoning, namely LARS and STARQL.

SOLUTION:
Reactive Multi-Context Systems: Heterogeneous Reasoning in Dynamic Environments