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restricted Boltzmann machine

Christophe Gardella, Olivier Marre, Thierry Mora
The brain has no direct access to physical stimuli but only to the spiking activity evoked in sensory organs. It is unclear how the brain can learn representations of the stimuli based on those noisy, correlated responses alone. Here we show how to build an accurate distance map of responses solely from the structure of the population activity of retinal ganglion cells. We introduce the Temporal Restricted Boltzmann Machine to learn the spatiotemporal structure of the population activity and use this model to define a distance between spike trains...
March 12, 2018: Proceedings of the National Academy of Sciences of the United States of America
Jingcong Li, Zhu Liang Yu, Zhenghui Gu, Wei Wu, Yuanqing Li, Lianwen Jin
Detecting and Please provide the correct one analyzing the event-related potential (ERP) remains an important problem in neuroscience. Due to the low signal-to-noise ratio and complex spatio-temporal patterns of ERP signals, conventional methods usually rely on ensemble averaging technique for reliable detection, which may obliterate subtle but important information in each trial of ERP signals. Inspired by deep learning methods, we propose a novel hybrid network termed ERP-NET. With hybrid deep structure, the proposed network is able to learn complex spatial and temporal patterns from single-trial ERP signals...
March 2018: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Xintao Hu, Heng Huang, Bo Peng, Junwei Han, Nian Liu, Jinglei Lv, Lei Guo, Christine Guo, Tianming Liu
Blind source separation (BSS) is commonly used in functional magnetic resonance imaging (fMRI) data analysis. Recently, BSS models based on restricted Boltzmann machine (RBM), one of the building blocks of deep learning models, have been shown to improve brain network identification compared to conventional single matrix factorization models such as independent component analysis (ICA). These models, however, trained RBM on fMRI volumes, and are hence challenged by model complexity and limited training set...
February 18, 2018: Human Brain Mapping
Peter de Boves Harrington
A modified algorithm for training a restricted Boltzmann machine (RBM) has been devised and demonstrated for improving the results for partial least squares (PLS) calibration of wheat and meat by near-infrared (NIR) spectroscopy. In all cases, the PLS calibrations improved by using the abstract features generated from the RBM so long as the nonlinear mapping increased the dimensionality. The evaluations were validated using bootstrapped Latin partitions (BLPs) with 5 bootstraps and 3-Latin partitions which proved useful because of the statistical learning and random initial conditions of the RBM networks...
June 20, 2018: Analytica Chimica Acta
Hwasuk Cho, Hyunwoo Son, Kihwan Seong, Byungsub Kim, Hong-June Park, Jae-Yoon Sim
This paper presents an IC implementation of on-chip learning neuromorphic autoencoder unit in a form of rate-based spiking neural network. With a current-mode signaling scheme embedded in a 500 × 500 6b SRAM-based memory, the proposed architecture achieves simultaneous processing of multiplications and accumulations. In addition, a transposable memory read for both forward and backward propagations and a virtual lookup table are also proposed to perform an unsupervised learning of restricted Boltzmann machine...
February 2018: IEEE Transactions on Biomedical Circuits and Systems
Yang Zhang, Ping Jiang, Hongyan Zhang, Peng Cheng
Thermal infrared remote sensing has become one of the main technology methods used for urban heat island research. When applying urban land surface temperature inversion of the thermal infrared band, problems with intensity level division arise because the method is subjective. However, this method is one of the few that performs heat island intensity level identification. This paper will build an intensity level identifier for an urban heat island, by using weak supervision and thought-based learning in an improved, restricted Boltzmann machine (RBM) model...
January 23, 2018: International Journal of Environmental Research and Public Health
Angelica I Aviles-Rivero, Samar M Alsaleh, Alicia Casals
PURPOSE: Technical advancements have been part of modern medical solutions as they promote better surgical alternatives that serve to the benefit of patients. Particularly with cardiovascular surgeries, robotic surgical systems enable surgeons to perform delicate procedures on a beating heart, avoiding the complications of cardiac arrest. This advantage comes with the price of having to deal with a dynamic target which presents technical challenges for the surgical system. In this work, we propose a solution for cardiac motion estimation...
January 19, 2018: International Journal of Computer Assisted Radiology and Surgery
Adriano Barra, Giuseppe Genovese, Peter Sollich, Daniele Tantari
We study generalized restricted Boltzmann machines with generic priors for units and weights, interpolating between Boolean and Gaussian variables. We present a complete analysis of the replica symmetric phase diagram of these systems, which can be regarded as generalized Hopfield models. We underline the role of the retrieval phase for both inference and learning processes and we show that retrieval is robust for a large class of weight and unit priors, beyond the standard Hopfield scenario. Furthermore, we show how the paramagnetic phase boundary is directly related to the optimal size of the training set necessary for good generalization in a teacher-student scenario of unsupervised learning...
October 2017: Physical Review. E
Sérgio Pereira, Raphael Meier, Richard McKinley, Roland Wiest, Victor Alves, Carlos A Silva, Mauricio Reyes
Machine learning systems are achieving better performances at the cost of becoming increasingly complex. However, because of that, they become less interpretable, which may cause some distrust by the end-user of the system. This is especially important as these systems are pervasively being introduced to critical domains, such as the medical field. Representation Learning techniques are general methods for automatic feature computation. Nevertheless, these techniques are regarded as uninterpretable "black boxes"...
December 20, 2017: Medical Image Analysis
Eunsuk Chong, Taejin Choi, Hyungmin Kim, Seung-Jong Kim, Yoha Hwang, Jong Min Lee
We propose a novel approach of selecting useful input sensors as well as learning a mathematical model for predicting lower limb joint kinematics. We applied a feature selection method based on the mutual information called the variational information maximization, which has been reported as the state-of-the-art work among information based feature selection methods. The main difficulty in applying the method is estimating reliable probability density of input and output data, especially when the data are high dimensional and real-valued...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Heather M O'Leary, Juan Manuel Mayor, Chi-Sang Poon, Walter E Kaufmann, Mustafa Sahin
Rett syndrome (RTT) is a severe neurodevelopmental disorder that can cause pervasive wakeful respiratory disturbances that include tachypnea, breath-holding, and central apnea. Quantitative analysis of these respiratory disturbances in RTT is considered a promising outcome measure for clinical trials. Currently, machine learning methodologies have not been employed to automate the classification of RTT respiratory disturbances. In this paper, we propose using temporal, flow, and autocorrelation features taken from the respiratory inductance plethsymography chest signal...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Xue Jiang, Han Zhang, Feng Duan, Xiongwen Quan
BACKGROUND: Predicting disease-associated genes is helpful for understanding the molecular mechanisms during the disease progression. Since the pathological mechanisms of neurodegenerative diseases are very complex, traditional statistic-based methods are not suitable for identifying key genes related to the disease development. Recent studies have shown that the computational models with deep structure can learn automatically the features of biological data, which is useful for exploring the characteristics of gene expression during the disease progression...
October 11, 2017: BMC Bioinformatics
Purvi Agrawal, Sriram Ganapathy
The modulation filtering approach to robust automatic speech recognition (ASR) is based on enhancing perceptually relevant regions of the modulation spectrum while suppressing the regions susceptible to noise. In this paper, a data-driven unsupervised modulation filter learning scheme is proposed using convolutional restricted Boltzmann machine. The initial filter is learned using the speech spectrogram while subsequent filters are learned using residual spectrograms. The modulation filtered spectrograms are used for ASR experiments on noisy and reverberant speech where these features provide significant improvements over other robust features...
September 2017: Journal of the Acoustical Society of America
Domingos S P Salazar
In this work, we analyze the nonequilibrium thermodynamics of a class of neural networks known as restricted Boltzmann machines (RBMs) in the context of unsupervised learning. We show how the network is described as a discrete Markov process and how the detailed balance condition and the Maxwell-Boltzmann equilibrium distribution are sufficient conditions for a complete thermodynamics description, including nonequilibrium fluctuation theorems. Numerical simulations in a fully trained RBM are performed and the heat exchange fluctuation theorem is verified with excellent agreement to the theory...
August 2017: Physical Review. E
Xun Gao, Lu-Ming Duan
Part of the challenge for quantum many-body problems comes from the difficulty of representing large-scale quantum states, which in general requires an exponentially large number of parameters. Neural networks provide a powerful tool to represent quantum many-body states. An important open question is what characterizes the representational power of deep and shallow neural networks, which is of fundamental interest due to the popularity of deep learning methods. Here, we give a proof that, assuming a widely believed computational complexity conjecture, a deep neural network can efficiently represent most physical states, including the ground states of many-body Hamiltonians and states generated by quantum dynamics, while a shallow network representation with a restricted Boltzmann machine cannot efficiently represent some of those states...
September 22, 2017: Nature Communications
Haiou Li, Jie Hou, Badri Adhikari, Qiang Lyu, Jianlin Cheng
BACKGROUND: Deep learning is one of the most powerful machine learning methods that has achieved the state-of-the-art performance in many domains. Since deep learning was introduced to the field of bioinformatics in 2012, it has achieved success in a number of areas such as protein residue-residue contact prediction, secondary structure prediction, and fold recognition. In this work, we developed deep learning methods to improve the prediction of torsion (dihedral) angles of proteins...
September 18, 2017: BMC Bioinformatics
Xin Chen, Jian Weng, Wei Lu, Jiaming Xu, Jiasi Weng
Learning deep representations have been applied in action recognition widely. However, there have been a few investigations on how to utilize the structural manifold information among different action videos to enhance the recognition accuracy and efficiency. In this paper, we propose to incorporate the manifold of training samples into deep learning, which is defined as deep manifold learning (DML). The proposed DML framework can be adapted to most existing deep networks to learn more discriminative features for action recognition...
September 15, 2017: IEEE Transactions on Neural Networks and Learning Systems
Ian McLoughlin, Jingjie Li, Yan Song, Hamid R Sharifzadeh
Statistical speech reconstruction for larynx-related dysphonia has achieved good performance using Gaussian mixture models and, more recently, restricted Boltzmann machine arrays; however, deep neural network (DNN)-based systems have been hampered by the limited amount of training data available from individual voice-loss patients. The authors propose a novel DNN structure that allows a partially supervised training approach on spectral features from smaller data sets, yielding very good results compared with the current state-of-the-art...
August 2017: Healthcare Technology Letters
Tae-Eui Kam, Heung-Il Suk, Seong-Whan Lee
Despite countless studies on autism spectrum disorder (ASD), diagnosis relies on specific behavioral criteria and neuroimaging biomarkers for the disorder are still relatively scarce and irrelevant for diagnostic workup. Many researchers have focused on functional networks of brain activities using resting-state functional magnetic resonance imaging (rsfMRI) to diagnose brain diseases, including ASD. Although some existing methods are able to reveal the abnormalities in functional networks, they are either highly dependent on prior assumptions for modeling these networks or do not focus on latent functional connectivities (FCs) by considering discriminative relations among FCs in a nonlinear way...
August 28, 2017: Human Brain Mapping
Xuan Peng, Xunzhang Gao, Yifan Zhang, Xiang Li
This paper proposes a new feature learning method for the recognition of radar high resolution range profile (HRRP) sequences. HRRPs from a period of continuous changing aspect angles are jointly modeled and discriminated by a single model named the discriminative infinite restricted Boltzmann machine (Dis-iRBM). Compared with the commonly used hidden Markov model (HMM)-based recognition method for HRRP sequences, which requires efficient preprocessing of the HRRP signal, the proposed method is an end-to-end method of which the input is the raw HRRP sequence, and the output is the label of the target...
July 20, 2017: Sensors
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