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:
Deep learning and reinforcement learning methods have recently been used to solve a variety of problems in continuous control domains. An obvious application of these techniques is dexterous manipulation tasks in robotics which are difficult to solve using traditional control theory or hand-engineered approaches. One example of such a task is to grasp an object and precisely stack it on another. Solving this difficult and practically relevant problem in the real world is an important long-term goal for the field of robotics. Here we take a step towards this goal by examining the problem in simulation and providing models and techniques aimed at solving it. We introduce two extensions to the Deep Deterministic Policy Gradient algorithm (DDPG), a model-free Q-learning based method, which make it significantly more data-efficient and scalable. Our results show that by making extensive use of off-policy data and replay, it is possible to find control policies that robustly grasp objects and stack them. Further, our results hint that it may soon be feasible to train successful stacking policies by collecting interactions on real robots.
answer:
Data-efficient Deep Reinforcement Learning for Dexterous Manipulation

question:
Microarrays are made it possible to simultaneously monitor the expression profiles of thousands of genes under various experimental conditions. It is used to identify the co-expressed genes in specific cells or tissues that are actively used to make proteins. This method is used to analysis the gene expression, an important task in bioinformatics research. Cluster analysis of gene expression data has proved to be a useful tool for identifying co-expressed genes, biologically relevant groupings of genes and samples. In this paper we applied K-Means with Automatic Generations of Merge Factor for ISODATAAGMFI. Though AGMFI has been applied for clustering of Gene Expression Data, this proposed Enhanced Automatic Generations of Merge Factor for ISODATAEAGMFI Algorithms overcome the drawbacks of AGMFI in terms of specifying the optimal number of clusters and initialization of good cluster centroids. Experimental results on Gene Expression Data show that the proposed EAGMFI algorithms could identify compact clusters with perform well in terms of the Silhouette Coefficients cluster measure.
answer:
Performance Analysis of Enhanced Clustering Algorithm for Gene Expression Data

question:
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.
answer:
Inference in Hybrid Networks: Theoretical Limits and Practical Algorithms