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.

Example Input: We marry ideas from deep neural networks and approximate Bayesian inference to derive a generalised class of deep, directed generative models, endowed with a new algorithm for scalable inference and learning. Our algorithm introduces a recognition model to represent approximate posterior distributions, and that acts as a stochastic encoder of the data. We develop stochastic backpropagation – rules for back-propagation through stochastic variables – and use this to develop an algorithm that allows for joint optimisation of the parameters of both the generative and recognition model. We demonstrate on several real-world data sets that the model generates realistic samples, provides accurate imputations of missing data and is a useful tool for high-dimensional data visualisation.
Example Output: Stochastic Back-propagation and Variational Inference in  Deep Latent Gaussian Models

Example Input: As a general and thus popular model for autonomous systems, partially observable Markov decision process (POMDP) can capture uncertainties from different sources like sensing noises, actuation errors, and uncertain environments. However, its comprehensiveness makes the planning and control in POMDP difficult. Traditional POMDP planning problems target to find the optimal policy to maximize the expectation of accumulated rewards. But for safety critical applications, guarantees of system performance described by formal specifications are desired, which motivates us to consider formal methods to synthesize supervisor for POMDP. With system specifications given by Probabilistic Computation Tree Logic (PCTL), we propose a supervisory control framework with a type of deterministic finite automata (DFA), za-DFA, as the controller form. While the existing work mainly relies on optimization techniques to learn fixed-size finite state controllers (FSCs), we develop an L∗ learning based algorithm to determine both space and transitions of za-DFA. Membership queries and different oracles for conjectures are defined. The learning algorithm is sound and complete. An example is given in detailed steps to illustrate the supervisor synthesis algorithm.
Example Output: Supervisor Synthesis of POMDP based on Automata Learning

Example Input: This paper proposes DRL-Sense—a multisense word representation learning model, to address the word sense ambiguity issue, where a sense selection module and a sense representation module are jointly learned in a reinforcement learning fashion. A novel reward passing procedure is proposed to enable joint training on the selection and representation modules. The modular design implements pure senselevel representation learning with linear time sense selection (decoding). We further develop a non-parametric learning algorithm and a sense exploration mechanism for better flexibility and robustness. The experiments on benchmark data show that the proposed approach achieves the state-of-the-art performance on contextual word similarities and comparable performance with Google’s word2vec while using much less training data.
Example Output:
DRL-Sense: Deep Reinforcement Learning for Multi-Sense Word Representations