This paper considers a demand response agent that must find a near-optimal sequence of decisions based on sparse observations of its environment. Extracting a relevant set of features from these observations is a challenging task and may require substantial domain knowledge. One way to tackle this problem is to store sequences of past observations and actions in the state vector, making it high...

Recently, the rapid development of word embedding and neural networks has brought new inspiration to various NLP and IR tasks. In this paper, we describe a staged hybrid model combining Recurrent Convolutional Neural Networks (RCNN) with highway layers. The highway network module is incorporated in the middle takes the output of the bidirectional Recurrent Neural Network (Bi-RNN) module in the ...

Deep convolutional neural networks are being actively investigated in a wide range of speech and audio processing applications including speech recognition, audio event detection and computational paralinguistics, owing to their ability to reduce factors of variations, for learning from speech. However, studies have suggested to favor a certain type of convolutional operations when building a d...

Deep learning is a trending topic widely studied by researchers due to increase in the abundance of data and getting meaningful results with them. Convolutional Neural Networks (CNN) is one of the most popular architectures used in deep learning. Binarized Neural Network (BNN) is also a neural network which consists of binary weights and activations. Neural Networks has large number of paramete...

We propose a convolutional neural network as an alternative to recurrent neural networks for separating out individual speakers in a sound mixture. Our results achieve state-of-the-art results with an order of magnitude fewer parameters. We also characterize the robustness of both models to generalize to three different testing conditions including a novel dataset. We create a new dataset RealT...

Deep learning refers to a shining branch of machine learning that is based on learning levels of representations. Convolutional Neural Networks (CNN) is one kind of deep neural network. It can study concurrently. In this article, we use convolutional neural network to implement the typical face recognition problem which can overcome the influence of pose or resolution in face recognition. Then,...

Adaptive learning is an important neural network characteristic; this means that they learn how to take care of difficult tasks by learning through illustrative samples of the problem to solve. Since neural networks can learn to tell the difference among many patterns by samples and training, there is no need to elaborate an a priori model, neither to develop specific probability distribution f...

Latest results indicate that features learned via convolutional neural networks outperform previous descriptors on classification tasks by a large margin. It has been shown that these networks still work well when they are applied to datasets or recognition tasks different from those they were trained on. However, descriptors like SIFT are not only used in recognition but also for many correspo...

Deep convolutional neural networks are generally regarded as robust function approximators. So far, this intuition is based on perturbations to external stimuli such as the images to be classified. Here we explore the robustness of convolutional neural networks to perturbations to the internal weights and architecture of the network itself. We show that convolutional networks are surprisingly r...

Use of neural networks for computer vision, speech recognition, and other applications has exploded in recent years, in part due to their unprecedented performance on a variety of benchmarks. Nonetheless, highthroughput and energy-efficient evaluation of such neural networks, and in particular, convolutional neural networks (CNNs), remains an active field of research. Evaluation of networks is ...