deep learning neural networks
Impact of Earnings per Share on Market Price of Share with Special Reference to Selected Companies Listed on NSE
... Deep learning algorithms transform their inputs through more layers than shallow learning ...feedforward neural network, the depth of the CAPs (thus of the network) is the number of hidden ...
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Multi-modal learning using deep neural networks
... Face recognition has seen significant progress in recent years and most of it can be attributed to metric learning loss functions that enhance the learning of the model. Several novel loss functions have ...
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Maximum Principle Based Algorithms for Deep Learning
... In deep learning, there are a few works that share our perspective of deep neural net- works as a discretization of a dynamical ...for deep learning that goes beyond gradient ...
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Online/offline score informed music signal decomposition: application to minus one
... Convolutional neural network; DL: Deep learning; DNN: Deep neural networks; DTW: Dynamic time warping; ED: Energy distribution; EUC: Euclidean distance; GT: Ground-truth; HMM: ...
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Biosignals learning and synthesis using deep neural networks
... “shallow” networks with the increase of hidden layers, complexity, com- putational power and learning ...of learning long-term dependencies by forgetting and updating the layers ...
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Application of deep neural networks for security analysis of digital infrastructure componentsa
... by learning such a network in the plurality of marked code ...LSTM networks [13, ...of neural network change in the order reverse to the order of signal passage through the network in the test ...
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Smart Education System Developed by Sentiment Analysis of Students Using PMM Neural Networks
... then deep learning helps in finding groups of such students from a geographical area who can group and study ...audience. Neural networks are widely used in finding patterns and helps in such ...
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Deep Learning: Approaches and Challenges
... One of the most challenging problems of CNN is its training time, it takes days even weeks to train a model for a very huge dataset. Instead of training for all the data that are redundant and noisy, Liang et. al. [64] ...
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Cascade recurring deep networks for audible range prediction
... a neural network leads to improvements in prediction ...of deep neural networks is to pile up many hidden layers between the input layer and the output layer ...a neural network becomes ...
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Reinforcement Learning with Deep Quantum Neural Networks
... of deep learning and RL, and by observing just the screen pixels, deep RL computer programs have beaten a world champion in the game Go and played many Atari 2600 video games better than humans [5] ...
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Superintelligent Deep Learning Artificial Neural Networks
... Artificial Neural Network to learn and make sense of something complicated and ...Artificial Neural Network would simply be a simple linear ...Artificial Neural Network more sophisticated and ...
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Understanding deep learning via backtracking and deconvolution
... Convolutional neural networks are widely adopted for solving problems in image clas- ...of deep learning through exploring the miss-classified cases in facial and emotion ...Keywords: ...
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Deep Learning Based Visual Tracking: A Review
... first neural-network tracker that combines convolutional and recurrent networks with RL algorithm in ...reinforcement learning (RL) agent making target location ...
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YNUWB at SemEval 2019 Task 6: K max pooling CNN with average meta embedding for identifying offensive language
... machine learning methods to obtain tagged data from different Twitter accounts in an inex- pensive way, and to learn the binary classifier- s of the “racist” and “nonracist” tags (Kwok and Wang, ...al ...
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DETECTION OF WHALES USING DEEP LEARNING METHODS AND NEURAL NETWORKS
... of deep learning techniques in machine learning it is possible to ask the machine to identify patterns and feature with proper ...training. Neural systems are effective example classifiers ...
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A New Vision About AI and Situation Awareness Model of Auto driving with Implicit Memory
... Deep learning (DL) has dramatically improved the state-of-the-art in image recognition 1–2 and speech recognition 3–4 , object detection and many other domains such as drug discovery, genomics 1 and ...
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Image Captioning using Multimodal Embedding
... Convolutional Neural Networks over image regions, bidirectional Recurrent Neural Networks over sentences, and a structured objective that aligns the two modalities through multimodal ...
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Assessing the Corpus Size vs Similarity Trade off for Word Embeddings in Clinical NLP
... of deep learning methods in natural language processing (NLP) and the large amounts of data they often require stands in stark contrast to the relatively data-poor clinical NLP ...used deep ...
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Weakly supervised learning via statistical sufficiency
... with deep neural networks, including recurrent neural ...with neural networks, our two-step learning procedure requires more ...whole learning process is summarized ...
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Scalable Database Indexing and Fast Image Retrieval Based on Deep Learning and Hierarchically Nested Structure Applied to Remote Sensing and Plant Biology
... CNN has achieved state-of-the-art performance in a variety of applications, including natural language processing [28,29], speech recognition [30], and object recognition [31]. Inspired by the success of CNN in many ...
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