instruction:
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.
question:
Recently, the problem of local minima in very high dimensional non-convex optimization has been challenged and the problem of saddle points has been introduced. This paper introduces a dynamic type of normalization that forces the system to escape saddle points. Unlike other saddle point escaping algorithms, second order information is not utilized, and the system can be trained with an arbitrary gradient descent learner. The system drastically improves learning in a range of deep neural networks on various data-sets in comparison to non-CPN neural networks. 1 SADDLE POINT PROBLEM 1.
answer:
CHARGED POINT NORMALIZATION AN EFFICIENT SOLUTION TO THE SADDLE POINT PROBLEM

question:
This paper is about reducing influence dia gram (ID) evaluation into Bayesian network (BN) inference problems. Such reduction is interesting because it enables one to read ily use one's favorite BN inference algorithm to efficiently evaluate IDs. Two such reduc tion methods have been proposed previously (Cooper 1988, Shachter and Peot 1992). This paper proposes a new method. The BN in ference problems induced by the mew method are much easier to solve than those induced by the two previous methods.
answer:
Probabilistic Inference in Influence Diagrams

question:
In this paper, we implicitly incorporate morpheme information into word embedding. Based on the strategy we utilize the morpheme information, three models are proposed. To test the performances of our models, we conduct the word similarity and syntactic analogy. The results demonstrate the effectiveness of our methods. Our models beat the comparative baselines on both tasks to a great extent. On the golden standard Wordsim-353 and RG-65, our models approximately outperform CBOW for 5 and 7 percent, respectively. In addition, 7 percent advantage is also achieved by our models on syntactic analysis. According to parameter analysis, our models can increase the semantic information in the corpus and our performances on the smallest corpus are similar to the performance of CBOW on the corpus which is five times ours. This property of our methods may have some positive effects on NLP researches about the corpus-limited languages.
answer:
Implicitly Incorporating Morphological Information into Word Embedding