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: We address the problem of contour detection via per-pixel classifications of edge point. To facilitate the process, the proposed approach leverages with DenseNet, an efficient implementation of multiscale convolutional neural networks (CNNs), to extract an informative feature vector for each pixel and uses an SVM classifier to accomplish contour detection. The main challenge lies in adapting a pre-trained per-image CNN model for yielding per-pixel image features. We propose to base on the DenseNet architecture to achieve pixelwise fine-tuning and then consider a cost-sensitive strategy to further improve the learning with a small dataset of edge and non-edge image patches. In the experiment of contour detection, we look into the effectiveness of combining per-pixel features from different CNN layers and obtain comparable performances to the state-of-the-art on BSDS500.

Output: CONTOUR DETECTION USING COST-SENSITIVE CON-


Input: Consider Input: Knowledge graph embedding aims to represent entities and relations in a large-scale knowledge graph as elements in a continuous vector space. Existing methods, e.g., TransE and TransH, learn embedding representation by defining a global margin-based loss function over the data. However, the optimal loss function is determined during experiments whose parameters are examined among a closed set of candidates. Moreover, embeddings over two knowledge graphs with different entities and relations share the same set of candidate loss functions, ignoring the locality of both graphs. This leads to the limited performance of embedding related applications. In this paper, we propose a locally adaptive translation method for knowledge graph embedding, called TransA, to find the optimal loss function by adaptively determining its margin over different knowledge graphs. Experiments on two benchmark data sets demonstrate the superiority of the proposed method, as compared to the-state-of-the-art ones.

Output: Locally Adaptive Translation for Knowledge Graph Embedding


Input: Consider Input: An important subclass of hybrid Bayesian networks are those that represent Conditional Linear Gaussian (CLG) distributionsa distribution with a multivari­ ate Gaussian component for each instantiation of the discrete variables. In this paper we explore the prob­ lem of inference in CLGs, and provide complexity re­ sults for an important class of CLGs, which includes Switching Kalman Filters. In particular, we prove that even if the CLG is restricted to an extremely simple structure of a polytree, the inference task is NP-hard. Furthermore, we show that, unless P=NP, even ap­ proximate inference on these simple networks is in­ tractable. Given the often prohibitive computational cost of even approximate inference, we must take advantage of spe­ cial domain properties which may enable efficient in­ ference. We concentrate on the fault diagnosis domain, and explore several approximate inference algorithms. These algorithms try to find a small subset of Gaus­ sians which are a good approximation to the full mix­ ture distribution. We consider two Monte Carlo ap­ proaches and a novel approach that enumerates mix­ ture components in order of prior probability. We com­ pare these methods on a variety of problems and show that our novel algorithm is very promising for large, hybrid diagnosis problems.


Output: Inference in Hybrid Networks: Theoretical Limits and Practical Algorithms