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:
The EM-algorithm is a general procedure to get maximum likelihood estimates if part of the observations on the variables of a network are missing. In this paper a stochastic version of the algorithm is adapted to probabilistic neural networks describing the associative dependency of variables. These networks have a proba­ bility distribution, which is a special case of the distribution generated by proba­ bilistic inference networks. Hence both types of networks can be combined al­ lowing to integrate probabilistic rules as well as unspecified associations in a sound way. The resulting network may have a number of interesting features including cycles of probabilistic rules, hidden 'un­ observable' variables, and uncertain and contradictory evidence.
answer:
Integrating Probabilistic Rules into Neural Networks: A Stochastic EM Learning Algorithm


question:
This paper shows how Long Short-term Memory recurrent neural networks can be used to generate complex sequences with long-range structure, simply by predicting one data point at a time. The approach is demonstrated for text (where the data are discrete) and online handwriting (where the data are real-valued). It is then extended to handwriting synthesis by allowing the network to condition its predictions on a text sequence. The resulting system is able to generate highly realistic cursive handwriting in a wide variety of styles.
answer:
Generating Sequences With Recurrent Neural Networks


question:
Designing an e-commerce recommender system that serves hundreds of millions of active users is a daunting challenge. Ranking strategy as the key module needs to be more carefully designed. We find two key factors that affect users’ behaviors: attractive item content and compatibility with users’ interests. To extract these factors, a ranking model needs to understand users from a human vision perspective. This paper proposes Telepath, a vision-based architecture that simulates the human vision system to extract the key visual signals that attract users to a displayed item and generate vision activations and simulates cerebral cortex to understand users’ interest based on the captured activations from browsed items. Telepath is a combination of CNN, RNN and DNN. In practice, the Telepath model has been launched to JD’s online recommender system and advertising system. For one of the major item recommendation blocks on the JD app, CTR, GMV and orders have increased 1.59%, 8.16% and 8.71% respectively. For several major advertising publishers of JD DSP, CTR, GMV and ROI have increased 6.58%, 61.72% and 65.57% respectively by the first launch, and further increased 2.95%, 41.75% and 41.37% respectively by the second launch.
answer:

Telepath: Understanding Users from a Human Vision Perspective in Large-Scale Recommender Systems