In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

Q: This work adopts the very successful distributional perspective on reinforcement learning and adapts it to the continuous control setting. We combine this within a distributed framework for off-policy learning in order to develop what we call the Distributed Distributional Deep Deterministic Policy Gradient algorithm, D4PG. We also combine this technique with a number of additional, simple improvements such as the use of N-step returns and prioritized experience replay. Experimentally we examine the contribution of each of these individual components, and show how they interact, as well as their combined contributions. Our results show that across a wide variety of simple control tasks, difficult manipulation tasks, and a set of hard obstacle-based locomotion tasks the D4PG algorithm achieves state of the art performance.

A: We develop an agent that we call the Distributional Deterministic Deep Policy Gradient algorithm, which achieves state of the art performance on a number of challenging continuous control problems.
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Q: Sentiment classification is an active research area with several applications including analysis of political opinions, classifying comments, movie reviews, news reviews and product reviews. To employ rule based sentiment classification, we require sentiment lexicons. However, manual construction of sentiment lexicon is time consuming and costly for resource-limited languages. To bypass manual development time and costs, we tried to build Amharic Sentiment Lexicons relying on corpus based approach. The intention of this approach is to handle sentiment terms specific to Amharic language from Amharic Corpus. Small set of seed terms are manually prepared from three parts of speech such as noun, adjective and verb. We developed algorithms for constructing Amharic sentiment lexicons automatically from Amharic news corpus. Corpus based approach is proposed relying on the word co-occurrence distributional embedding including frequency based embedding (i.e. Positive Point-wise Mutual Information PPMI). First we build word-context unigram frequency count matrix and transform it to point-wise mutual Information matrix. Using this matrix, we computed the cosine distance of mean vector of seed lists and each word in the corpus vocabulary. Based on the threshold value, the top closest words to the mean vector of seed list are added to the lexicon. Then the mean vector of the new sentiment seed list is updated and process is repeated until we get sufficient terms in the lexicon. Using PPMI with threshold value of 100 and 200, we got corpus based Amharic Sentiment lexicons of size 1811 and 3794 respectively by expanding 519 seeds. Finally, the lexicon generated in corpus based approach is evaluated.


A: Corpus based Algorithm is developed generate Amharic Sentiment lexicon relying on corpus
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Q: Deep neural networks have been tremendously successful in a number of tasks.
 One of the main reasons for this is their capability to automatically
 learn representations of data in levels of abstraction,
 increasingly disentangling the data as the internal transformations are applied.
 In this paper we propose a novel regularization method that penalize covariance between dimensions of the hidden layers in a network, something that benefits the disentanglement.
 This makes the network learn nonlinear representations that are linearly uncorrelated, yet allows the model to obtain good results on a number of tasks, as demonstrated by our experimental evaluation.
 The proposed technique can be used to find the dimensionality of the underlying data, because it effectively disables dimensions that aren't needed.
 Our approach is simple and computationally cheap, as it can be applied as a regularizer to any gradient-based learning model.

A:
We propose a novel regularization method that penalize covariance between dimensions of the hidden layers in a network.
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