Convolutional Recurrent Neural Network (CRNN) google
This paper proposes a novel framework for detecting redundancy in supervised sentence categorisation. Unlike traditional singleton neural network, our model incorporates character-aware convolutional neural network (Char-CNN) with character-aware recurrent neural network (Char-RNN) to form a convolutional recurrent neural network (CRNN). Our model benefits from Char-CNN in that only salient features are selected and fed into the integrated Char-RNN. Char-RNN effectively learns long sequence semantics via sophisticated update mechanism. We compare our framework against the state-of-the-art text classification algorithms on four popular benchmarking corpus. For instance, our model achieves competing precision rate, recall ratio, and F1 score on the Google-news data-set. For twenty-news-groups data stream, our algorithm obtains the optimum on precision rate, recall ratio, and F1 score. For Brown Corpus, our framework obtains the best F1 score and almost equivalent precision rate and recall ratio over the top competitor. For the question classification collection, CRNN produces the optimal recall rate and F1 score and comparable precision rate. We also analyse three different RNN hidden recurrent cells’ impact on performance and their runtime efficiency. We observe that MGU achieves the optimal runtime and comparable performance against GRU and LSTM. For TFIDF based algorithms, we experiment with word2vec, GloVe, and sent2vec embeddings and report their performance differences. …

Pervasive Analytics google
During eras of global economic shifts, there was always a key resource discovered that became the spark of transformation for groups of individuals that could effectively harness it. Today, that resource is data. In no uncertain terms, we are witnessing a global data rush and leading companies realize that data will grow enterprise over the next several decades as much as any capital asset. These forward-looking companies realize that to be successful, enterprises must leverage analytics in order to create a more predictable and valuable organization. In some cases they must package data in a way that adds value and informs employees, or their customers, by deploying analytics into decisions making processes everywhere. This idea is referred to as pervasive analytics. But to drive a pervasive analytics strategy and win the data rush, successful companies also recognize the need to transform the way they think about data management and processes in order to unlock the true value of data. …

Collaborative Deep Reinforcement Learning (CDRL) google
Besides independent learning, human learning process is highly improved by summarizing what has been learned, communicating it with peers, and subsequently fusing knowledge from different sources to assist the current learning goal. This collaborative learning procedure ensures that the knowledge is shared, continuously refined, and concluded from different perspectives to construct a more profound understanding. The idea of knowledge transfer has led to many advances in machine learning and data mining, but significant challenges remain, especially when it comes to reinforcement learning, heterogeneous model structures, and different learning tasks. Motivated by human collaborative learning, in this paper we propose a collaborative deep reinforcement learning (CDRL) framework that performs adaptive knowledge transfer among heterogeneous learning agents. Specifically, the proposed CDRL conducts a novel deep knowledge distillation method to address the heterogeneity among different learning tasks with a deep alignment network. Furthermore, we present an efficient collaborative Asynchronous Advantage Actor-Critic (cA3C) algorithm to incorporate deep knowledge distillation into the online training of agents, and demonstrate the effectiveness of the CDRL framework using extensive empirical evaluation on OpenAI gym. …

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