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检索条件"主题词=autoencoder"

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Deep Learning Based Reliable and Intelligent Chaotic OFDM Communications for Cognitive Radio System 10

Deep Learning Based Reliable and Intelligent Chaotic OFDM Co...

引用

10th International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Lin, Xintong Zhang, Lin Sun Yat Sen Univ Sch Elect & Informat Technol Guangzhou 510006 Guangdong Peoples R China

ISBN: (纸本)9781538661192

In this paper, we present a deep learning based modulation scheme for chaotic orthogonal frequency division multiplex (OFDM) transmissions over non-contiguous frequency bands of cognitive radio systems. In cognitive radio systems, the users access the spectrum bands dynamically and the corresponding channel characteristics also changes. Different from the traditional modulation scheme that uses the fixed mapping pattern to modulate the signals, we propose to apply the deep learning method to build up the constellations intelligently. Based on the autoencoder architecture of deep learning, we construct the constellation mapping and demapping patterns adaptively with the aim to minimize the bit error rate (BER) over the dynamically changing non-contiguous channels. Simulation results over additive white Gaussian noise (AWGN) channel and Rayleigh fading channel show that our proposed system achieves better BER performances for legitimate receivers when compared with the conventional modulation schemes. In addition, the presented scheme remains the high security performance thanks to the usage of the chaotic sequences.

关键词： autoencoder Bit error rate Chaotic orthogonal frequency division multiplex Cognitive Radio Deep learning

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DEEP LEARNING CROP CLASSIFICATION APPROACH BASED ON SPARSE CODING OF TIME SERIES OF SATELLITE DATA 38

DEEP LEARNING CROP CLASSIFICATION APPROACH BASED ON SPARSE C...

引用

38th IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Lavreniuk, Mykola Kussul, Nataliia Novikov, Alexei SSAU NASU Space Res Inst Kiev Ukraine Natl Tech Univ Ukraine Igor Sikorsky Kiev Polytech Inst Kiev Ukraine

ISBN: (纸本)9781538671504

Crop classification maps based on high resolution remote sensing data are essential for supporting sustainable land management. The most challenging problems for their producing are collecting of ground based training and validation datasets, non-regular satellite data acquisition and cloudiness. To increase the efficiency of ground data utilization it is important to develop classifiers able to be trained on the data collected in the previous year. In this study, we propose new deep learning method for providing crop classification maps using in-situ data that has been collected in the previous year. Main idea of the study is to utilize deep learning approach based on sparse autoencoder. At the first stage it is trained on satellite data only and then neural network fine-tuning is conducted based on in-situ data form the previous year. Taking into account that collecting ground truth data is very time consuming and challenging task, the proposed approach allows us to avoid necessity for annual collecting in-situ data for the same territory. Experimental results for the territory of Ukraine show that this technique is rather efficient and provides reliable crop classification maps with overall accuracy higher than 85.9%.

关键词： deep learning autoencoder neural network sparse coding crop mapping Sentinel-1

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Contextual Bandit with Adaptive Feature Extraction 18

Contextual Bandit with Adaptive Feature Extraction

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18th IEEE International Conference on Data Mining Workshops (ICDMW)

作者： Lin, Baihan Bouneffouf, Djallel Cecchi, Guillermo A. Rish, Irina Columbia Univ New York NY 10027 USA IBM Thomas J Watson Res Ctr Yorktown Hts NY USA

ISBN: (纸本)9781538692882

We consider an online decision making setting known as contextual bandit problem, and propose an approach for improving contextual bandit performance by using an adaptive feature extraction (representation learning) based on online clustering. Our approach starts with an off-line pre-training on unlabeled history of contexts (which can be exploited by our approach, but not by the standard contextual bandit), followed by an online selection and adaptation of encoders. Specifically, given an input sample (context), the proposed approach selects the most appropriate encoding function to extract a feature vector which becomes an input for a contextual bandit, and updates both the bandit and the encoding function based on the context and on the feedback (reward). Our experiments on a variety of datasets, and both in stationary and non-stationary environments of several kinds demonstrate clear advantages of the proposed adaptive representation learning over the standard contextual bandit based on "raw" input contexts.

关键词： multi-arm bandit contextual bandit online learning autoencoder representation learning online clustering

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EXTENDED PIPELINE FOR CONTENT-BASED FEATURE ENGINEERING IN MUSIC GENRE RECOGNITION

EXTENDED PIPELINE FOR CONTENT-BASED FEATURE ENGINEERING IN M...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Raissi, Tina Tibo, Alessandro Bientinesi, Paolo Rhein Westfal TH Aachen AICES Schinkelstr 2 D-52062 Aachen Germany Univ Florence Dept Informat Engn Via S Marta 3 I-50139 Florence Italy

ISBN: (纸本)9781538646588

We present a feature engineering pipeline for the construction of musical signal characteristics, to be used for the design of a supervised model for musical genre identification. The key idea is to extend the traditional two-step process of extraction and classification with additive stand-alone phases which are no longer organized in a waterfall scheme. The whole system is realized by traversing backtrack arrows and cycles between various stages. In order to give a compact and effective representation of the features, the standard early temporal integration is combined with other selection and extraction phases: on the one hand, the selection of the most meaningful characteristics based on information gain, and on the other hand, the inclusion of the nonlinear correlation between this subset of features, determined by an autoencoder. The results of the experiments conducted on GTZAN dataset reveal a noticeable contribution of this methodology towards the model's performance in classification task.

关键词： Musical signal genre classification feature extraction and selection information gain autoencoder

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A semantic space approach for automatic summarization of documents 9

A semantic space approach for automatic summarization of doc...

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9th IEEE International Conference on Cognitive Infocommunications (CogInfoCom)

作者： Kaszas, Valer Tundik, Mate Akos Szaszak, Gyorgy Budapest Univ Technol & Econ Dept Telecommun & Media Informat Budapest Hungary

ISBN: (纸本)9781538670941

Automatic document summarization is used to extract or generate short, but rich snippets which represent well the essential meaning and key information contained in documents. Classical approaches of extractive summarization mostly rely on bag-of-words models or a graph representation reflecting word neighbourhood information to obtain a ranking of the sentences of the document. The higher the rank, the more relevant is the sentence for the summary. Given word-embeddings have recently been shown to represent semantic meaning of individual words in natural languages, intuitively it seems to be a straightforward way to carry out such a ranking in the latent space reflecting the meaning (semantics), not just the form (syntax). Vector operations in the semantic space have been shown to be highly consistent with alternations of the meaning reflected by such operations (i.e. analogical reasoning tasks). In the present paper we show that simply cumulatively adding semantic space vectors to represent sentence level meaning already yields comparable performance to the state-of-the-art in document summarization. The additive property of semantic representations for high number of component words is considered an important and promising outcome of this research for cognitive infocommunication applications.

关键词： word-embedding semantics autoencoder summarization latent space

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Public Health Surveillance System for Online Social Networks using One-Class Text Classification 6

Public Health Surveillance System for Online Social Networks...

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6th International Conference on Control Engineering and Information Technology (CEIT)

作者： Tahir, Bilal Amjad, Kamran Firdous, Samar Mehmood, Muhammad Amir UET Al Khawarizmi Inst Comp Sci Lahore Pakistan King Edward Med Univ Lahore Pakistan

ISBN: (纸本)9781538676417

Public health surveillance by traditional means is a costly and time consuming process. Today, the widespread use of social media has enabled researchers to study different aspects of life such as health, lifestyle, etc. Anonymous postings on these forums enable people to benefit from the collective experience of others facing similar problems. To effectively discern target data from the outliers in a web corpus, an efficient mechanism is required. Traditional approaches such as keyword-based filtering results in the loss of relevant data due to limited vocabulary and lack of contextual information. In this paper, we present a data filtration framework based on Long short-term memory (LSTM) recurrent neural network model for one-class text classification. We compare similarity of regenerated texts using this model for each disease with the original text using Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metric for outlier filtration and classification. Optimal value of ROUGE similarity threshold is determined by introducing an optimization parameter that minimizes the misclassification rate. Leveraging data from three major online health forums, we show that our classification technique outperforms keyword-based filtering and conventional approach of multi-class text classification. Our classification technique can be effectively used for online social networks, search engines, and online recommender systems.

关键词： LSTM Text classification autoencoder Deep Learning Online Health Forums

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Learning as Performance: Autoencoding and Generating Dance Movements in Real Time 1

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7th International Conference on Computational Intelligence in Music, Sound, Art and Design (EvoMUSART) / 14th European Event on Biologically Inspired Music, Sound, Art and Design

作者： Berman, Alexander James, Valencia AI Am Gothenburg Sweden AI Am Redwood City CA USA

ISBN: (数字)9783319775838

ISBN: (纸本)9783319775838;9783319775821

This paper describes the technology behind a performance where human dancers interact with an "artificial" performer projected on a screen. The system learns movement patterns from the human dancers in real time. It can also generate novel movement sequences that go beyond what it has been taught, thereby serving as a source of inspiration for the human dancers, challenging their habits and normal boundaries and enabling a mutual exchange of movement ideas. It is central to the performance concept that the system's learning process is perceivable for the audience. To this end, an autoencoder neural network is trained in real time with motion data captured live on stage. As training proceeds, a "pose map" emerges that the system explores in a kind of improvisational state. The paper shows how this method is applied in the performance, and shares observations and lessons made in the process.

关键词： Human-computer co-creativity Movement generation Real-time learning autoencoder

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EEG-BASED EMOTION RECOGNITION USING MACHINE LEARNING TECHNIQUES

EEG-BASED EMOTION RECOGNITION USING MACHINE LEARNING TECHNIQ...

引用

作者： LAN ZIRUI Nanyang Technological University

学位级别：博士

Electroencephalography (EEG)-based emotion recognition attempts to detect the affective states of humans directly via spontaneous EEG signals, bypassing the peripheral nervous system. In this thesis, we explore various machine learning techniques for EEG-based emotion recognition, and focus on the three research gaps outlined as follows. 1. Stable feature selection for recalibration-less affective Brain-Computer Interfaces 2. Cross-subject transfer learning for calibration-less affective Brain- Computer Interfaces 3. Unsupervised feature learning for affective Brain-Computer Interfaces We propose several novel methods in this thesis to address the three research gaps and validate our proposed methods by experiments. Extensive comparisons between our methods and other existing methods justify the advantages of our methods.

关键词： Electroencephalography (EEG) affective Brain-Computer Interface (aBCI) stable affective features feature selection classification recalibration-less aBCI domain adaptation cross-subject transfer learning cross-dataset transfer learning unsupervised feature extraction neural networks autoencoder

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An Extreme Learning Machine Based Pretraining Method for Multi-Layer Neural Networks 10

An Extreme Learning Machine Based Pretraining Method for Mul...

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Joint 10th International Conference on Soft Computing and Intelligent Systems (SCIS) / 19th International Symposium on Advanced Intelligent Systems (ISIS)

作者： Noinongyao, Pavit Watchareeruetai, Ukrit King Mongkuts Inst Technol Ladkrabang Int Coll Chalongkrung Rd Bangkok 10520 Thailand

ISBN: (纸本)9781538626337

One approach in training a deep neural network to perform effectively is to do unsupervised pretraining on each layer, followed by fine-tuning the whole network. A common way is to train an unsupervised model of neural network such as restricted Boltzmann machines or autoencoders and stack them on top of another. Although these unsupervised pretraining approaches yield good performance, relying on back-propagation, due to iterative learning process, they still suffer from a long pretraining time. Extreme learning machine (ELM) is an analytical training approach which is extremely fast and gives a solution with a good generalization performance. In this paper, we apply a new ELM based unsupervised learning, named backward ELM based autoencoder (BELM-AE), to pretrain each layer of a neural network before using a back-propagation based learning algorithm to fine-tune the whole network. Experimental results show that the new pretraining method requires significantly shorter training time and also yields better testing performance on various datasets.

关键词： extreme learning machine pretraining autoencoder backward extreme learning machine

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Learning to Decode 7T-Like MR Image Reconstruction from 3T MR Images 4th

Learning to Decode 7T-Like MR Image Reconstruction from 3T M...

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4th International Workshop on Deep Learning in Medical Image Analysis (DLMIA) / 8th International Workshop on Multimodal Learning for Clinical Decision Support (ML-CDS)

作者： Sharma, Aditya Kaur, Prabhjot Nigam, Aditya Bhavsar, Arnav Indian Inst Technol Sch Comp & Elect Engn Mandi Himachal Prades India

ISBN: (纸本)9783030008895;9783030008888

Increasing demand for high field magnetic resonance (MR) scanner indicates the need for high-quality MR images for accurate medical diagnosis. However, cost constraints, instead, motivate a need for algorithms to enhance images from low field scanners. We propose an approach to process the given low field (3T) MR image slices to reconstruct the corresponding high field (7T-like) slices. Our framework involves a novel architecture of a merged convolutional autoencoder with a single encoder and multiple decoders. Specifically, we employ three decoders with random initializations, and the proposed training approach involves selection of a particular decoder in each weight-update iteration for back propagation. We demonstrate that the proposed algorithm outperforms some related contemporary methods in terms of performance and reconstruction time.

关键词： autoencoder Multiple decoders Low-field MRI Reconstruction High-field MRI

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