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检索条件"任意字段=2017 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2017"

共 1667 条记录，以下是41-50 订阅

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learning APPROXIMATE NEURAL ESTIMATORS FOR WIRELESS CHANNEL STATE INFORMATION

LEARNING APPROXIMATE NEURAL ESTIMATORS FOR WIRELESS CHANNEL ...

引用

27th ieee international workshop on machine learning for signal processing (mlsp)

作者： O'Shea, Tim Karra, Kiran Clancy, T. Charles Virginia Tech Dept Elect & Comp Engn Arlington VA 22203 USA

ISBN: (纸本)9781509063413

Estimation is a critical component of synchronization in wireless and signal processing systems. There is a rich body of work on estimator derivation, optimization, and statistical characterization from analytic system models which are used pervasively today. We explore an alternative approach to building estimators which relies principally on approximate regression using large datasets and large computationally efficient artificial neural network models capable of learning non-linear function mappings which provide compact and accurate estimates. For single carrier PSK modulation, we explore the accuracy and computational complexity of such estimators compared with the current gold-standard analytically derived alternatives. We compare performance in various wireless operating conditions and consider the trade offs between the two different classes of systems. Our results show the learned estimators can provide improvements in areas such as short-time estimation and estimation under non-trivial real world channel conditions such as fading or other non-linear hardware or propagation effects.

关键词： Estimation Timing Frequency estimation Computational modeling Fading channels signal to noise ratio Phase shift keying

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RENYI ENTROPY BASED MUTUAL INFORMATION FOR SEMI-SUPERVISED BIRD VOCALIZATION SEGMENTATION

RENYI ENTROPY BASED MUTUAL INFORMATION FOR SEMI-SUPERVISED B...

引用

27th ieee international workshop on machine learning for signal processing (mlsp)

作者： Thakur, Anshul Abrol, Vinayak Sharma, Pulkit Rajan, Padmanabhan Indian Inst Technol Sch Comp & Elect Engn Mandi India

ISBN: (纸本)9781509063413

In this paper we describe a semi-supervised algorithm to segment bird vocalizations using matrix factorization and Renyi entropy based mutual information. Singular value decomposition (SVD) is applied on pooled time-frequency representations of bird vocalizations to learn basis vectors. By utilizing only a few of the bases, a compact feature representation is obtained for input test data. Renyi entropy based mutual information is calculated between feature representations of consecutive frames. After some simple post-processing, a threshold is used to reliably distinguish bird vocalizations from other sounds. The algorithm is evaluated on the field recordings of different bird species and different SNR conditions. The results highlight the effectiveness of the proposed method in all SNR conditions, improvements over other methods, and its generality.

关键词： Bird call segmentation feature learning using PCA Renyi entropy

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learning GUIDED CONVOLUTIONAL NEURAL NETWORKS FOR CROSS-RESOLUTION FACE RECOGNITION

LEARNING GUIDED CONVOLUTIONAL NEURAL NETWORKS FOR CROSS-RESO...

引用

27th ieee international workshop on machine learning for signal processing (mlsp)

作者： Fu, Tzu-Chien Chiu, Wei-Chen Wang, Yu-Chiang Frank Acad Sinica Res Ctr Informat Technol Innovat Taipei Taiwan Natl Chiao Tung Univ Hsinchiu Taiwan

ISBN: (纸本)9781509063413

Cross-resolution face recognition tackles the problem of matching face images with different resolutions. Although state-of-the-art convolutional neural network (CNN) based methods have reported promising performances on standard face recognition problems, such models cannot sufficiently describe images with resolution different from those seen during training, and thus cannot solve the above task accordingly. In this paper, we propose Guided Convolutional Neural Network (Guided-CNN), which is a novel CNN architecture with parallel sub-CNN models as guide and learners. Unique loss functions are introduced, which would serve as joint supervision for images within and across resolutions. Our experiments not only verify the use of our model for cross-resolution recognition, but also its applicability of recognizing face images with different degrees of occlusion.

关键词： Face recognition deep learning convolutional neural networks

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THE LINEAR PROCESS MIXTURE MODEL

THE LINEAR PROCESS MIXTURE MODEL

引用

23rd ieee international workshop on machine learning for signal processing (mlsp)

作者： Palmer, Jason A. Kreutz-Delgado, Ken Makeig, Scott Univ Calif San Diego Swartz Ctr Computat Neurosci La Jolla CA 92093 USA Univ Calif San Diego Dept Elect & Comp Engn La Jolla CA 92093 USA

ISBN: (纸本)9781479911806

We consider a likelihood framework for analyzing multivariate time series as mixtures of independent linear processes. We propose a flexible, Newton algorithm for estimating impulse response functions associated with independent linear processes and an EM-based finite mixture model to handle intermittent regimes. Simulations and application to EEG are also provided.

关键词： Newton method electroencephalography expectation-maximisation algorithm medical signal processing time series

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DEEP DIVERGENCE-BASED CLUSTERING

DEEP DIVERGENCE-BASED CLUSTERING

引用

27th ieee international workshop on machine learning for signal processing (mlsp)

作者： Kampffmeyer, M. Lokse, S. Bianchi, F. M. Livi, L. Salberg, A. -B. Jenssen, R. UiT Arctic Univ Norway UiT Machine Learning Grp Tromso Norway Univ Exeter Dept Comp Sci Exeter Devon England Norwegian Comp Ctr Oslo Norway

ISBN: (纸本)9781509063413

A promising direction in deep learning research is to learn representations and simultaneously discover cluster structure in unlabeled data by optimizing a discriminative loss function. Contrary to supervised deep learning, this line of research is in its infancy and the design and optimization of a suitable loss function with the aim of training deep neural networks for clustering is still an open challenge. In this paper, we propose to leverage the discriminative power of information theoretic divergence measures, which have experienced success in traditional clustering, to develop a new deep clustering network. Our proposed loss function incorporates explicitly the geometry of the output space, and facilitates fully unsupervised training end-to-end. Experiments on real datasets show that the proposed algorithm achieves competitive performance with respect to other state-of-the-art methods.

关键词： Deep learning clustering information theoretic learning

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MIML-AI: Mixed-supervision multi-instance multi-label learning with auxiliary information

MIML-AI: Mixed-supervision multi-instance multi-label learni...

引用

2017 ieee international workshop on machine learning for signal processing, mlsp 2017

作者： Nguyen, Tarn Raich, Raviv Fern, Xiaoli Z. Pham, Anh T. School of EECS Oregon State University CorvallisOR97331-5501 United States

ISBN: (纸本)9781509063413

Manual labeling of individual instances is time-consuming. This is commonly resolved by labeling a bag-of-instances with a single common label or label-set. However, this approach is still time-costly for large datasets. In this paper, we propose a mixed-supervision multi-instance multi-label learning model for learning from easily available meta data information (MIML-AI). This auxiliary information is normally collected automatically with the data, e.g., an image location information or a document author name. We propose a discriminative graphical model with exact inferences to train a classifier based on auxiliary label information and a small number of labeled bags. This strategy utilizes meta data as means of providing a weaker label as an alternative to intensive manual labeling. Experiment on real data illustrates the effectiveness of our proposed method relative to current approaches, which do not use the information from bags that contain only meta-data label information. © 2017 ieee.

关键词： Metadata

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NEAREST NEIGHBOR-BASED IMPORTANCE WEIGHTING

NEAREST NEIGHBOR-BASED IMPORTANCE WEIGHTING

引用

22nd ieee international workshop on machine learning for signal processing (mlsp)

作者： Loog, Marco Delft Univ Technol Pattern Recognit Lab NL-2600 AA Delft Netherlands

ISBN: (纸本)9781467310260

Importance weighting is widely applicable in machine learning in general and in techniques dealing with data co-variate shift problems in particular. A novel, direct approach to determine such importance weighting is presented. It relies on a nearest neighbor classification scheme and is relatively straightforward to implement. Comparative experiments on various classification tasks demonstrate the effectiveness of our so-called nearest neighbor weighting (NNeW) scheme. Considering its performance, our procedure can act as a simple and effective baseline method for importance weighting.

关键词： learning systems

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UPPER BOUND PERFORMANCE OF SEMI-DEFINITE PROGRAMMING FOR LOCALISATION IN INHOMOGENEOUS MEDIA

UPPER BOUND PERFORMANCE OF SEMI-DEFINITE PROGRAMMING FOR LOC...

引用

27th ieee international workshop on machine learning for signal processing (mlsp)

作者： Nadimi, E. S. Blanes-Vidal, V. Univ Southern Denmark Maersk Mc Kinney Moller Inst Embodied Syst Robot & Learning Campusvej 55 DK-5230 Odense Denmark

ISBN: (纸本)9781509063413

In this paper, we regarded an absorbing inhomogeneous medium as an assembly of thin layers having different propagation properties. We derived a stochastic model for the refractive index and formulated the localisation problem given noisy distance measurements using graph realisation problem. We relaxed the problem using semi-definite programming (SDP) approach in l(p) realisation domain and derived upper bounds that follow Edmundson-Madansky bound of order 6p (EM6p) on the SDP objective function to provide an estimation of the techniques' localisation accuracy. Our results showed that the inhomogeneity of the media and the choice of l(p) norm have significant impact on the ratio of the expected value of the localisation error to the upper bound for the expected optimal SDP objective value. The tightest ratio was derived when l(infinity) norm was used.

关键词： Semi-Definite programming inhomogeneous media localisation Edmundson-Madansky bound

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DEEP REINFORCEMENT learning BASED ENERGY BEAMFORMING FOR POWERING SENSOR NETWORKS 29

DEEP REINFORCEMENT LEARNING BASED ENERGY BEAMFORMING FOR POW...

引用

ieee 29th international workshop on machine learning for signal processing (mlsp)

作者： Ozcelikkale, Ayca Koseoglu, Mehmet Srivastava, Mani Ahlen, Anders Uppsala Univ Signals & Syst Uppsala Sweden Hacettepe Univ Dept Comp Sci Ankara Turkey Univ Calif Dept Elect & Comp Engn Davis CA USA

ISBN: (纸本)9781728108247

We focus on a wireless sensor network powered with an energy beacon, where sensors send their measurements to the sink using the harvested energy. The aim of the system is to estimate an unknown signal over the area of interest as accurately as possible. We investigate optimal energy beamforming at the energy beacon and optimal transmit power allocation at the sensors under non-linear energy harvesting models. We use a deep reinforcement learning (RL) based approach where multi-layer neural networks are utilized. We illustrate how RL can approach the optimum performance without explicitly forming a system model, but suffers from slow convergence. We also quantify the importance of the number of antennas at the energy beamformer and the number of sensors.

关键词： Wireless communications energy harvesting resource allocation reinforcement learning neural networks

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MINI-BATCH STOCHASTIC APPROACHES FOR ACCELERATED MULTIPLICATIVE UPDATES IN NONNEGATIVE MATRIX FACTORISATIONWITH BETA-DIVERGENCE 26

MINI-BATCH STOCHASTIC APPROACHES FOR ACCELERATED MULTIPLICAT...

引用

26th ieee international workshop on machine learning for signal processing (mlsp)

作者： Serizel, Romain Essid, Slim Richard, Gael Univ Paris Saclay LTCI CNRS Telecom ParisTech F-75013 Paris France

ISBN: (纸本)9781509007462

Nonnegative matrix factorisation (NMF) with beta-divergence is a popular method to decompose real world data. In this paper we propose mini-batch stochastic algorithms to perform NMF efficiently on large data matrices. Besides the stochastic aspect, the mini-batch approach allows exploiting intensive computing devices such as general purpose graphical processing units to decrease the processing time and in some cases outperform coordinate descent approach.

关键词： Nonnegative matrix factorisation GPGPU multiplicative rules online learning

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