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Journal articles on the topic 'Noisy-OR model'

Author: Grafiati

Published: 6 September 2023

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1

Quintanar-Gago, David A., and Pamela F. Nelson. "The extended Recursive Noisy OR model: Static and dynamic considerations." International Journal of Approximate Reasoning 139 (December 2021): 185–200. http://dx.doi.org/10.1016/j.ijar.2021.09.013.

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Zhou, Kuang, Arnaud Martin, and Quan Pan. "The Belief Noisy-OR Model Applied to Network Reliability Analysis." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 24, no. 06 (2016): 937–60. http://dx.doi.org/10.1142/s0218488516500434.

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One difficulty faced in knowledge engineering for Bayesian Network (BN) is the quantification step where the Conditional Probability Tables (CPTs) are determined. The number of parameters included in CPTs increases exponentially with the number of parent variables. The most common solution is the application of the so-called canonical gates. The Noisy-OR (NOR) gate, which takes advantage of the independence of causal interactions, provides a logarithmic reduction of the number of parameters required to specify a CPT. In this paper, an extension of NOR model based on the theory of belief functi

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Li, W., P. Poupart, and P. Van Beek. "Exploiting Structure in Weighted Model Counting Approaches to Probabilistic Inference." Journal of Artificial Intelligence Research 40 (April 19, 2011): 729–65. http://dx.doi.org/10.1613/jair.3232.

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Previous studies have demonstrated that encoding a Bayesian network into a SAT formula and then performing weighted model counting using a backtracking search algorithm can be an effective method for exact inference. In this paper, we present techniques for improving this approach for Bayesian networks with noisy-OR and noisy-MAX relations---two relations that are widely used in practice as they can dramatically reduce the number of probabilities one needs to specify. In particular, we present two SAT encodings for noisy-OR and two encodings for noisy-MAX that exploit the structure or semantic

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Büttner, Martha, Lisa Schneider, Aleksander Krasowski, Joachim Krois, Ben Feldberg, and Falk Schwendicke. "Impact of Noisy Labels on Dental Deep Learning—Calculus Detection on Bitewing Radiographs." Journal of Clinical Medicine 12, no. 9 (2023): 3058. http://dx.doi.org/10.3390/jcm12093058.

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Supervised deep learning requires labelled data. On medical images, data is often labelled inconsistently (e.g., too large) with varying accuracies. We aimed to assess the impact of such label noise on dental calculus detection on bitewing radiographs. On 2584 bitewings calculus was accurately labeled using bounding boxes (BBs) and artificially increased and decreased stepwise, resulting in 30 consistently and 9 inconsistently noisy datasets. An object detection network (YOLOv5) was trained on each dataset and evaluated on noisy and accurate test data. Training on accurately labeled data yield

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Shang, Yuming, He-Yan Huang, Xian-Ling Mao, Xin Sun, and Wei Wei. "Are Noisy Sentences Useless for Distant Supervised Relation Extraction?" Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 8799–806. http://dx.doi.org/10.1609/aaai.v34i05.6407.

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The noisy labeling problem has been one of the major obstacles for distant supervised relation extraction. Existing approaches usually consider that the noisy sentences are useless and will harm the model's performance. Therefore, they mainly alleviate this problem by reducing the influence of noisy sentences, such as applying bag-level selective attention or removing noisy sentences from sentence-bags. However, the underlying cause of the noisy labeling problem is not the lack of useful information, but the missing relation labels. Intuitively, if we can allocate credible labels for noisy sen

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Zheng, Guoqing, Ahmed Hassan Awadallah, and Susan Dumais. "Meta Label Correction for Noisy Label Learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 12 (2021): 11053–61. http://dx.doi.org/10.1609/aaai.v35i12.17319.

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Leveraging weak or noisy supervision for building effective machine learning models has long been an important research problem. Its importance has further increased recently due to the growing need for large-scale datasets to train deep learning models. Weak or noisy supervision could originate from multiple sources including non-expert annotators or automatic labeling based on heuristics or user interaction signals. There is an extensive amount of previous work focusing on leveraging noisy labels. Most notably, recent work has shown impressive gains by using a meta-learned instance re-weight

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Maeda, Shin-ichi, Wen-Jie Song, and Shin Ishii. "Nonlinear and Noisy Extension of Independent Component Analysis: Theory and Its Application to a Pitch Sensation Model." Neural Computation 17, no. 1 (2005): 115–44. http://dx.doi.org/10.1162/0899766052530866.

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In this letter, we propose a noisy nonlinear version of independent component analysis (ICA). Assuming that the probability density function (p.d.f.) of sources is known, a learning rule is derived based on maximum likelihood estimation (MLE). Our model involves some algorithms of noisy linear ICA (e.g., Bermond & Cardoso, 1999) or noise-free nonlinear ICA (e.g., Lee, Koehler, & Orglmeister, 1997) as special cases. Especially when the nonlinear function is linear, the learning rule derived as a generalized expectation-maximization algorithm has a similar form to the noisy ICA algorithm

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Zhan, Peida, Hong Jiao, Kaiwen Man, and Lijun Wang. "Using JAGS for Bayesian Cognitive Diagnosis Modeling: A Tutorial." Journal of Educational and Behavioral Statistics 44, no. 4 (2019): 473–503. http://dx.doi.org/10.3102/1076998619826040.

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In this article, we systematically introduce the just another Gibbs sampler (JAGS) software program to fit common Bayesian cognitive diagnosis models (CDMs) including the deterministic inputs, noisy “and” gate model; the deterministic inputs, noisy “or” gate model; the linear logistic model; the reduced reparameterized unified model; and the log-linear CDM (LCDM). Further, we introduce the unstructured latent structural model and the higher order latent structural model. We also show how to extend these models to consider polytomous attributes, the testlet effect, and longitudinal diagnosis. F

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Hong, Zhiwei, Xiaocheng Fan, Tao Jiang, and Jianxing Feng. "End-to-End Unpaired Image Denoising with Conditional Adversarial Networks." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 4140–49. http://dx.doi.org/10.1609/aaai.v34i04.5834.

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Image denoising is a classic low level vision problem that attempts to recover a noise-free image from a noisy observation. Recent advances in deep neural networks have outperformed traditional prior based methods for image denoising. However, the existing methods either require paired noisy and clean images for training or impose certain assumptions on the noise distribution and data types. In this paper, we present an end-to-end unpaired image denoising framework (UIDNet) that denoises images with only unpaired clean and noisy training images. The critical component of our model is a noise l

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Kağan Akkaya, Emre, and Burcu Can. "Transfer learning for Turkish named entity recognition on noisy text." Natural Language Engineering 27, no. 1 (2020): 35–64. http://dx.doi.org/10.1017/s1351324919000627.

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AbstractIn this article, we investigate using deep neural networks with different word representation techniques for named entity recognition (NER) on Turkish noisy text. We argue that valuable latent features for NER can, in fact, be learned without using any hand-crafted features and/or domain-specific resources such as gazetteers and lexicons. In this regard, we utilize character-level, character n-gram-level, morpheme-level, and orthographic character-level word representations. Since noisy data with NER annotation are scarce for Turkish, we introduce a transfer learning model in order to

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Jittawiriyanukoon, Chanintorn. "Estimation of regression-based model with bulk noisy data." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 5 (2019): 3649. http://dx.doi.org/10.11591/ijece.v9i5.pp3649-3656.

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<span>The bulk noise has been provoking a contributed data due to a communication network with a tremendously low signal to noise ratio. An appreciated method for revising massive noise of individuals through information theory is widely discussed. One of the practical applications of this approach for bulk noise estimation is analyzed using intelligent automation and machine learning tools, dealing the case of bulk noise existence or nonexistence. A regression-based model is employed for the investigation and experiment. Estimation for the practical case with bulk noisy datasets is prop

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Hanafusa, Ryo, and Takeshi Okadome. "Bayesian Kernel Regression for Noisy Inputs Based on Nadaraya–Watson Estimator Constructed from Noiseless Training Data." Advances in Data Science and Adaptive Analysis 12, no. 01 (2020): 2050004. http://dx.doi.org/10.1142/s2424922x20500047.

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In regression for noisy inputs, noise is typically removed from a given noisy input if possible, and then the resulting noise-free input is provided to the regression function. In some cases, however, there is no available time or method for removing noise. The regression method proposed in this paper determines a regression function for noisy inputs using the estimated posterior of their noise-free constituents with a nonparametric estimator for noiseless explanatory values, which is constructed from noiseless training data. In addition, a probabilistic generative model is presented for estim

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Bhagawati, Rupam, and Thiruselvan Subramanian. "Quantum-aided feature selection model – A quantum machine learning approach." Journal of Discrete Mathematical Sciences & Cryptography 26, no. 3 (2023): 641–55. http://dx.doi.org/10.47974/jdmsc-1735.

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The accuracy of information retrieval systems is measured by the relevancy of retrieved results as per the user’s query. Relevant results are presented by performing various methods viz. indexing and crawling and the output of these processes is the retrieved results that have to pass through the ranking process which is the central goal of information retrieval systems. The ranking is carried out through the classification or clustering of processed results which can include redundant and noisy features. The accuracy of classification or clusters for the ranking process can be maximized by re

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Surin, V. A. "ON PROCESSING NOISY CONTRAST IMAGES." Bulletin of the South Ural State University series "Mathematics. Mechanics. Physics" 13, no. 1 (2021): 14–21. http://dx.doi.org/10.14529/mmph210102.

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The problem of noise reduction at sharp transitions of brightness in digital noisy contrast images is considered. In addition to the useful signal, digital images obtained by digitizing an analogue signal with a digital photo matrix have a noise component. Moreover, to obtain a digital image in the standard RGB color model, a demosaicing interpolation algorithm must be applied to the image obtained from a digital photo matrix. Due to such transformations, the Gaussian distribution of noise in a digital noisy image is violated. Using a standard image digitization model for noise reduction is no

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Hossain, Md Nahid, Samiul Basir, Md Shakhawat Hosen, et al. "Supervised Single Channel Speech Enhancement Method Using UNET." Electronics 12, no. 14 (2023): 3052. http://dx.doi.org/10.3390/electronics12143052.

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This paper proposes an innovative single-channel supervised speech enhancement (SE) method based on UNET, a convolutional neural network (CNN) architecture that expands on a few changes in the basic CNN architecture. In the training phase, short-time Fourier transform (STFT) is exploited on the noisy time domain signal to build a noisy time-frequency domain signal which is called a complex noisy matrix. We take the real and imaginary parts of the complex noisy matrix and concatenate both of them to form the noisy concatenated matrix. We apply UNET to the noisy concatenated matrix for extractin

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Liu, Kun-Lin, Wu-Jun Li, and Minyi Guo. "Emoticon Smoothed Language Models for Twitter Sentiment Analysis." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (2021): 1678–84. http://dx.doi.org/10.1609/aaai.v26i1.8353.

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Twitter sentiment analysis (TSA) has become a hot research topic in recent years. The goal of this task is to discover the attitude or opinion of the tweets, which is typically formulated as a machine learning based text classification problem. Some methods use manually labeled data to train fully supervised models, while others use some noisy labels, such as emoticons and hashtags, for model training. In general, we can only get a limited number of training data for the fully supervised models because it is very labor-intensive and time-consuming to manually label the tweets. As for the model

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Kourehli, Seyed Sina. "Damage Assessment in Structures Using Incomplete Modal Data and Artificial Neural Network." International Journal of Structural Stability and Dynamics 15, no. 06 (2015): 1450087. http://dx.doi.org/10.1142/s0219455414500874.

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This paper presents a novel approach for structural damage detection and estimation using incomplete noisy modal data and artificial neural network (ANN). A feed-forward back propagation network is proposed for estimating the structural damage location and severity. Incomplete modal data is used in the dynamic analysis of damaged structures by the condensed finite element model and as input parameters to the neural network for damage identification. In all cases, the first two natural modes were used for the training process. The present method is applied to three examples consisting of a simp

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Lindner, John F., Brian K. Meadows, Tracey L. Marsh, William L. Ditto, and Adi R. Bulsara. "Can Neurons Distinguish Chaos from Noise?" International Journal of Bifurcation and Chaos 08, no. 04 (1998): 767–81. http://dx.doi.org/10.1142/s0218127498000565.

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Recent studies suggesting evidence for determinism in the stochastic activity of the heart and brain have sparked an important scientific debate: Do biological systems exploit chaos or are they merely noisy? Here, we analyze the spike interval statistics of a simple integrate-and-fire model neuron to investigate how a real neuron might process noise and chaos, and possibly differentiate between the two. In some cases, our model neuron readily distinguishes noise from chaos, even discriminating among chaos characterized by different Lyapunov exponents. However, in other cases, the model neuron

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Guan, Qingji, Qinrun Chen, and Yaping Huang. "An Improved Heteroscedastic Modeling Method for Chest X-ray Image Classification with Noisy Labels." Algorithms 16, no. 5 (2023): 239. http://dx.doi.org/10.3390/a16050239.

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Chest X-ray image classification suffers from the high inter-similarity in appearance that is vulnerable to noisy labels. The data-dependent and heteroscedastic characteristic label noise make chest X-ray image classification more challenging. To address this problem, in this paper, we first revisit the heteroscedastic modeling (HM) for image classification with noise labels. Rather than modeling all images in one fell swoop as in HM, we instead propose a novel framework that considers the noisy and clean samples separately for chest X-ray image classification. The proposed framework consists

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Wei, Penghui, Wenji Mao, and Guandan Chen. "A Topic-Aware Reinforced Model for Weakly Supervised Stance Detection." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 7249–56. http://dx.doi.org/10.1609/aaai.v33i01.33017249.

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Analyzing public attitudes plays an important role in opinion mining systems. Stance detection aims to determine from a text whether its author is in favor of, against, or neutral towards a given target. One challenge of this task is that a text may not explicitly express an attitude towards the target, but existing approaches utilize target content alone to build models. Moreover, although weakly supervised approaches have been proposed to ease the burden of manually annotating largescale training data, such approaches are confronted with noisy labeling problem. To address the above two issue

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Kittisuwan, Pichid. "Low-complexity image denoising based on mixture model and simple form of MMSE estimation." International Journal of Wavelets, Multiresolution and Information Processing 16, no. 06 (2018): 1850052. http://dx.doi.org/10.1142/s0219691318500522.

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In order to enhance efficiency of artificial intelligence (AI) tools such as classification or pattern recognition, it is important to have noise-free data to be processed with AI tools. Therefore, the study of algorithms used for reducing noise is also very significant. In thermal condition, Gaussian noise is important problem in analog circuit and image processing. Therefore, this paper focuses on the study of an algorithm for Gaussian noise reduction. In recent year, Bayesian with wavelet-based methods provides good efficiency in noise reduction and spends short time in processing. In Bayes

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Mendonça, J. Ricardo G. "The inactive–active phase transition in the noisy additive (exclusive-or) probabilistic cellular automaton." International Journal of Modern Physics C 27, no. 02 (2015): 1650016. http://dx.doi.org/10.1142/s0129183116500169.

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We investigate the inactive–active phase transition in an array of additive (exclusive-or) cellular automata (CA) under noise. The model is closely related with the Domany-Kinzel (DK) probabilistic cellular automaton (PCA), for which there are rigorous as well as numerical estimates on the transition probabilities. Here, we characterize the critical behavior of the noisy additive cellular automaton by mean field analysis and finite-size scaling and show that its phase transition belongs to the directed percolation universality class of critical behavior. As a by-product of our analysis, we arg

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Mousavi, Hamid, Mareike Buhl, Enrico Guiraud, Jakob Drefs, and Jörg Lücke. "Inference and Learning in a Latent Variable Model for Beta Distributed Interval Data." Entropy 23, no. 5 (2021): 552. http://dx.doi.org/10.3390/e23050552.

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Latent Variable Models (LVMs) are well established tools to accomplish a range of different data processing tasks. Applications exploit the ability of LVMs to identify latent data structure in order to improve data (e.g., through denoising) or to estimate the relation between latent causes and measurements in medical data. In the latter case, LVMs in the form of noisy-OR Bayes nets represent the standard approach to relate binary latents (which represent diseases) to binary observables (which represent symptoms). Bayes nets with binary representation for symptoms may be perceived as a coarse a

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PARK, HYUNG-MIN, JONG-HWAN LEE, TAESU KIM, et al. "MODELING AUDITORY PATHWAY FOR INTELLIGENT INFORMATION ACQUISITION." International Journal of Information Acquisition 01, no. 04 (2004): 345–56. http://dx.doi.org/10.1142/s0219878904000367.

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An auditory model has been developed for an intelligent speech information acquisition system in real-world noisy environment. The developed mathematical model of the human auditory pathway consists of three components, i.e. the nonlinear feature extraction from cochlea to auditory cortex, the binaural processing at superior olivery complex, and the top-down attention from higher brain to the cochlea. The feature extraction is based on information-theoretic sparse coding throughout the auditory pathway. Also, the time-frequency masking is incorporated as a model of the lateral inhibition in bo

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Fawwaz, Dzaky Zakiyal, and Sang-Hwa Chung. "Real-Time and Robust Hydraulic System Fault Detection via Edge Computing." Applied Sciences 10, no. 17 (2020): 5933. http://dx.doi.org/10.3390/app10175933.

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We consider fault detection in a hydraulic system that maintains multivariate time-series sensor data. Such a real-world industrial environment could suffer from noisy data resulting from inaccuracies in hardware sensing or external interference. Thus, we propose a real-time and robust fault detection method for hydraulic systems that leverages cooperation between cloud and edge servers. The cloud server employs a new approach that includes a genetic algorithm (GA)-based feature selection that identifies feature-to-label correlations and feature-to-feature redundancies. A GA can efficiently pr

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Wang, Xiao Fei, Bo Nian Li, Yan Li Huang, and Xin Ran Wang. "Feature Extraction from Noisy Image Using Intersecting Cortical Model." Applied Mechanics and Materials 40-41 (November 2010): 516–22. http://dx.doi.org/10.4028/www.scientific.net/amm.40-41.516.

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This paper introduces an efficient approach for feature extraction from noisy image using Intersecting Cortical Model(ICM), which is a simplified model of Pulse-Coupled Neural Network(PCNN). In our research, the entropy sequence of the output image, is obtained from the original gray image by ICM, as feature vector of the gray image, which can be used to represent the gray image, and this has been proved by our experiments. Consequently, it is used in the image classification, and the mean square error (MSE) between the feature vector of the input image and the standard feature vector is used

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Qin, Tianyun, Rangding Wang, Diqun Yan, and Lang Lin. "Source Cell-Phone Identification in the Presence of Additive Noise from CQT Domain." Information 9, no. 8 (2018): 205. http://dx.doi.org/10.3390/info9080205.

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With the widespread availability of cell-phone recording devices, source cell-phone identification has become a hot topic in multimedia forensics. At present, the research on the source cell-phone identification in clean conditions has achieved good results, but that in noisy environments is not ideal. This paper proposes a novel source cell-phone identification system suitable for both clean and noisy environments using spectral distribution features of constant Q transform (CQT) domain and multi-scene training method. Based on the analysis, it is found that the identification difficulty lies

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Lazic, Nevena, Amarnag Subramanya, Michael Ringgaard, and Fernando Pereira. "Plato: A Selective Context Model for Entity Resolution." Transactions of the Association for Computational Linguistics 3 (December 2015): 503–15. http://dx.doi.org/10.1162/tacl_a_00154.

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We present Plato, a probabilistic model for entity resolution that includes a novel approach for handling noisy or uninformative features, and supplements labeled training data derived from Wikipedia with a very large unlabeled text corpus. Training and inference in the proposed model can easily be distributed across many servers, allowing it to scale to over 107 entities. We evaluate Plato on three standard datasets for entity resolution. Our approach achieves the best results to-date on TAC KBP 2011 and is highly competitive on both the CoNLL 2003 and TAC KBP 2012 datasets.

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Noh, Kyungjoo, Liang Jiang, and Bill Fefferman. "Efficient classical simulation of noisy random quantum circuits in one dimension." Quantum 4 (September 11, 2020): 318. http://dx.doi.org/10.22331/q-2020-09-11-318.

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Understanding the computational power of noisy intermediate-scale quantum (NISQ) devices is of both fundamental and practical importance to quantum information science. Here, we address the question of whether error-uncorrected noisy quantum computers can provide computational advantage over classical computers. Specifically, we study noisy random circuit sampling in one dimension (or 1D noisy RCS) as a simple model for exploring the effects of noise on the computational power of a noisy quantum device. In particular, we simulate the real-time dynamics of 1D noisy random quantum circuits via m

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Zhang, Tian Chi, Jian Pei Zhang, Jing Zhang, and Melvyn L. Smith. "Two-Step Modified Nash Equilibrium Method for Medical Image Segmentation Based on an Improved C-V Model." Journal of Medical Imaging and Health Informatics 8, no. 9 (2018): 1826–34. http://dx.doi.org/10.1166/jmihi.2018.2521.

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One of the most established region-based segmentation methods is the region based C-V model. This method formulates the image segmentation problem as a level set or improved level set clustering problem. However, the existing level set C-V model fails to perform well in the presence of noisy and incomplete data or when there is similarity between the objects and background, especially for clustering or segmentation tasks in medical images where objects appear vague and poorly contrasted in greyscale. In this paper, we modify the level set C-V model using a two-step modified Nash equilibrium ap

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Monson, Christopher K., and Kevin D. Seppi. "A Graphical Model for Evolutionary Optimization." Evolutionary Computation 16, no. 3 (2008): 289–313. http://dx.doi.org/10.1162/evco.2008.16.3.289.

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We present a statistical model of empirical optimization that admits the creation of algorithms with explicit and intuitively defined desiderata. Because No Free Lunch theorems dictate that no optimization algorithm can be considered more efficient than any other when considering all possible functions, the desired function class plays a prominent role in the model. In particular, this provides a direct way to answer the traditionally difficult question of what algorithm is best matched to a particular class of functions. Among the benefits of the model are the ability to specify the function

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Yang, Lei, Haiqing Zhang, Daiwei Li, Fei Xiao, and Shanglin Yang. "Facial Expression Recognition Based on Transfer Learning and SVM." Journal of Physics: Conference Series 2025, no. 1 (2021): 012015. http://dx.doi.org/10.1088/1742-6596/2025/1/012015.

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Abstract The facial expression datasets always have a problem: data with small amount or large amounts of data but also with large noisy. Both problems will affect the facial expression recognition accuracy of the model. A transfer learning method for facial expression recognition is proposed by combining the Convolutional Neural Network (CNN) and Support Vector Machine (SVM). SVM have good performance on small data sets and CNN based on transfer learning have better ability of feature extraction for large noisy data set. This method reduces the training time of model and increase the facial e

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Cedeño, Angel L., Ricardo Albornoz, Rodrigo Carvajal, Boris I. Godoy, and Juan C. Agüero. "A Two-Filter Approach for State Estimation Utilizing Quantized Output Data." Sensors 21, no. 22 (2021): 7675. http://dx.doi.org/10.3390/s21227675.

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Filtering and smoothing algorithms are key tools to develop decision-making strategies and parameter identification techniques in different areas of research, such as economics, financial data analysis, communications, and control systems. These algorithms are used to obtain an estimation of the system state based on the sequentially available noisy measurements of the system output. In a real-world system, the noisy measurements can suffer a significant loss of information due to (among others): (i) a reduced resolution of cost-effective sensors typically used in practice or (ii) a digitaliza

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Peeters, Bert, and Ard Kuijpers. "Classification of noisy vehicles from unsupervised measurements." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 265, no. 5 (2023): 2175–84. http://dx.doi.org/10.3397/in_2022_0312.

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The NEMO-project (https://nemo-cities.eu/) aims to identify noisy and polluting road and rail vehicles, using remote sensing technology. Noise levels from individual road vehicles are measured from the roadside, in normal traffic. Road authorities may use these data to enforce noise limits, to limit access to Low Emission Zones or to influence driving behaviour. Whether a vehicle is a 'high noise emitter' is a complex question, as the noise level depends on vehicle type and condition, driving style, weather and location-specific characteristics. From a legal perspective, the question may be an

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Abdel Qader, Akram. "A New Novel Hybrid Dynamic Color Segmentation Model for Road Signs in Noisy Conditions." International Journal of Software Innovation 9, no. 3 (2021): 1–22. http://dx.doi.org/10.4018/ijsi.2021070101.

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Image segmentation is the most important process in road sign detection and classification systems. In road sign systems, the spatial information of road signs are very important for safety issues. Road sign segmentation is a complex segmentation task because of the different road sign colors and shapes that make it difficult to use specific threshold. Most road sign segmentation studies do good in ideal situations, but many problems need to be solved when the road signs are in poor lighting and noisy conditions. This paper proposes a hybrid dynamic threshold color segmentation technique for r

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Rymarczyk, Tomasz, Krzysztof Polakowski, and Jan Sikora. "A NEW CONCEPT OF DISCRETIZATION MODEL FOR IMAGING IMPROVING IN ULTRASOUND TRANSMISSION TOMOGRAPHY." Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska 9, no. 4 (2019): 48–51. http://dx.doi.org/10.35784/iapgos.131.

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In this paper a new version of discretization model for Ultrasonic Transmission Tomography is presented. The algorithm has been extensively tested for synthetic noisy data on various configurations of internal objects. In order to improve the imaging quality, the pixels/voxels have been enlarged compared to the figures inscribed in pixels/voxels however no more than figures described on the standard square pixels or cubic voxels. The proposed algorithm provides better quality of imaging.

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Ji, S., and X. Yuan. "A GENERIC PROBABILISTIC MODEL AND A HIERARCHICAL SOLUTION FOR SENSOR LOCALIZATION IN NOISY AND RESTRICTED CONDITIONS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 3, 2016): 193–98. http://dx.doi.org/10.5194/isprsarchives-xli-b1-193-2016.

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A generic probabilistic model, under fundamental Bayes’ rule and Markov assumption, is introduced to integrate the process of mobile platform localization with optical sensors. And based on it, three relative independent solutions, bundle adjustment, Kalman filtering and particle filtering are deduced under different and additional restrictions. We want to prove that first, Kalman filtering, may be a better initial-value supplier for bundle adjustment than traditional relative orientation in irregular strips and networks or failed tie-point extraction. Second, in high noisy conditions, particl

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Ji, S., and X. Yuan. "A GENERIC PROBABILISTIC MODEL AND A HIERARCHICAL SOLUTION FOR SENSOR LOCALIZATION IN NOISY AND RESTRICTED CONDITIONS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 3, 2016): 193–98. http://dx.doi.org/10.5194/isprs-archives-xli-b1-193-2016.

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A generic probabilistic model, under fundamental Bayes’ rule and Markov assumption, is introduced to integrate the process of mobile platform localization with optical sensors. And based on it, three relative independent solutions, bundle adjustment, Kalman filtering and particle filtering are deduced under different and additional restrictions. We want to prove that first, Kalman filtering, may be a better initial-value supplier for bundle adjustment than traditional relative orientation in irregular strips and networks or failed tie-point extraction. Second, in high noisy conditions, particl

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Mayhew, Stephen, Gupta Nitish, and Dan Roth. "Robust Named Entity Recognition with Truecasing Pretraining." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 8480–87. http://dx.doi.org/10.1609/aaai.v34i05.6368.

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Although modern named entity recognition (NER) systems show impressive performance on standard datasets, they perform poorly when presented with noisy data. In particular, capitalization is a strong signal for entities in many languages, and even state of the art models overfit to this feature, with drastically lower performance on uncapitalized text. In this work, we address the problem of robustness of NER systems in data with noisy or uncertain casing, using a pretraining objective that predicts casing in text, or a truecaser, leveraging unlabeled data. The pretrained truecaser is combined

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Zhang, Jun, Wen Yao, Xiaoqian Chen, and Ling Feng. "Transferable Post-hoc Calibration on Pretrained Transformers in Noisy Text Classification." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 11 (2023): 13940–48. http://dx.doi.org/10.1609/aaai.v37i11.26632.

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Recent work has demonstrated that pretrained transformers are overconfident in text classification tasks, which can be calibrated by the famous post-hoc calibration method temperature scaling (TS). Character or word spelling mistakes are frequently encountered in real applications and greatly threaten transformer model safety. Research on calibration under noisy settings is rare, and we focus on this direction. Based on a toy experiment, we discover that TS performs poorly when the datasets are perturbed by slight noise, such as swapping the characters, which results in distribution shift. We

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Bocquet, Marc, Julien Brajard, Alberto Carrassi, and Laurent Bertino. "Data assimilation as a learning tool to infer ordinary differential equation representations of dynamical models." Nonlinear Processes in Geophysics 26, no. 3 (2019): 143–62. http://dx.doi.org/10.5194/npg-26-143-2019.

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Abstract. Recent progress in machine learning has shown how to forecast and, to some extent, learn the dynamics of a model from its output, resorting in particular to neural networks and deep learning techniques. We will show how the same goal can be directly achieved using data assimilation techniques without leveraging on machine learning software libraries, with a view to high-dimensional models. The dynamics of a model are learned from its observation and an ordinary differential equation (ODE) representation of this model is inferred using a recursive nonlinear regression. Because the met

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Zhang, Rui, Zhenghao Chen, Sanxing Zhang, et al. "Remote Sensing Image Scene Classification with Noisy Label Distillation." Remote Sensing 12, no. 15 (2020): 2376. http://dx.doi.org/10.3390/rs12152376.

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The widespread applications of remote sensing image scene classification-based Convolutional Neural Networks (CNNs) are severely affected by the lack of large-scale datasets with clean annotations. Data crawled from the Internet or other sources allows for the most rapid expansion of existing datasets at a low-cost. However, directly training on such an expanded dataset can lead to network overfitting to noisy labels. Traditional methods typically divide this noisy dataset into multiple parts. Each part fine-tunes the network separately to improve performance further. These approaches are inef

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Tang, Ning, Zi-Long Fan, and Hao-Sheng Zeng. "Improving the quality of noisy spatial quantum channels." Quantum Information and Computation 15, no. 7&8 (2015): 568–81. http://dx.doi.org/10.26421/qic15.7-8-3.

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We show, for the non-Markovian or time-dependent Markovian model of noise, by breaking the noisy spatial quantum channel (SQC) into a series of periodically arranged sub-components, that the quality of information transmission described by the purity, fidelity and concurrence of the output states can be improved. The physical mechanism and possible implementation of the idea have been discussed.

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Rohling, Jos H. T., and Janusz M. Meylahn. "Two-Community Noisy Kuramoto Model Suggests Mechanism for Splitting in the Suprachiasmatic Nucleus." Journal of Biological Rhythms 35, no. 2 (2020): 158–66. http://dx.doi.org/10.1177/0748730419898314.

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Recent mathematical results for the noisy Kuramoto model on a 2-community network may explain some phenomena observed in the functioning of the suprachiasmatic nucleus (SCN). Specifically, these findings might explain the types of transitions to a state of the SCN in which 2 components are dissociated in phase, for example, in phase splitting. In contrast to previous studies, which required additional time-delayed coupling or large variation in the coupling strengths and other variations in the 2-community model to exhibit the phase-split state, this model requires only the 2-community structu

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Klawonn, Matthew, Eric Heim, and James Hendler. "Exploiting Class Learnability in Noisy Data." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4082–89. http://dx.doi.org/10.1609/aaai.v33i01.33014082.

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In many domains, collecting sufficient labeled training data for supervised machine learning requires easily accessible but noisy sources, such as crowdsourcing services or tagged Web data. Noisy labels occur frequently in data sets harvested via these means, sometimes resulting in entire classes of data on which learned classifiers generalize poorly. For real world applications, we argue that it can be beneficial to avoid training on such classes entirely. In this work, we aim to explore the classes in a given data set, and guide supervised training to spend time on a class proportional to it

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Guo, Zhenyu, Yujuan Sun, Muwei Jian, and Xiaofeng Zhang. "Deep Residual Network with Sparse Feedback for Image Restoration." Applied Sciences 8, no. 12 (2018): 2417. http://dx.doi.org/10.3390/app8122417.

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A deep neural network is difficult to train due to a large number of unknown parameters. To increase trainable performance, we present a moderate depth residual network for the restoration of motion blurring and noisy images. The proposed network has only 10 layers, and the sparse feedbacks are added in the middle and the last layers, which are called FbResNet. FbResNet has fast convergence speed and effective denoising performance. In addition, it can also reduce the artificial Mosaic trace at the seam of patches, and visually pleasant output results can be produced from the blurred images or

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Northcutt, Curtis, Lu Jiang, and Isaac Chuang. "Confident Learning: Estimating Uncertainty in Dataset Labels." Journal of Artificial Intelligence Research 70 (April 14, 2021): 1373–411. http://dx.doi.org/10.1613/jair.1.12125.

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Learning exists in the context of data, yet notions of confidence typically focus on model predictions, not label quality. Confident learning (CL) is an alternative approach which focuses instead on label quality by characterizing and identifying label errors in datasets, based on the principles of pruning noisy data, counting with probabilistic thresholds to estimate noise, and ranking examples to train with confidence. Whereas numerous studies have developed these principles independently, here, we combine them, building on the assumption of a class-conditional noise process to directly esti

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Wu, Biao, Yong Huang, Xiang Chen, Sridhar Krishnaswamy, and Hui Li. "Guided-wave signal processing by the sparse Bayesian learning approach employing Gabor pulse model." Structural Health Monitoring 16, no. 3 (2016): 347–62. http://dx.doi.org/10.1177/1475921716665252.

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Guided waves have been used for structural health monitoring to detect damage or defects in structures. However, guided wave signals often involve multiple modes and noise. Extracting meaningful damage information from the received guided wave signal becomes very challenging, especially when some of the modes overlap. The aim of this study is to develop an effective way to deal with noisy guided-wave signals for damage detection as well as for de-noising. To achieve this goal, a robust sparse Bayesian learning algorithm is adopted. One of the many merits of this technique is its good performan

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Kaur, Inderjit, and Dr Pardeep Saini. "Classifier Model using Artificial Neural Network." International Journal of Engineering, Business and Management 7, no. 4 (2023): 69–73. http://dx.doi.org/10.22161/ijebm.7.4.11.

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When it comes to AI and ML, precision in categorization is of the utmost importance. In this research, the use of supervised instance selection (SIS) to improve the performance of artificial neural networks (ANNs) in classification is investigated. The goal of SIS is to enhance the accuracy of future classification tasks by identifying and selecting a subset of examples from the original dataset. The purpose of this research is to provide light on how useful SIS is as a preprocessing tool for artificial neural network-based classification. The work aims to improve the input dataset to ANNs by

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Stark, Oliver, Martin Pfeifer, and Sören Hohmann. "Parameter and Order Identification of Fractional Systems with Application to a Lithium-Ion Battery." Mathematics 9, no. 14 (2021): 1607. http://dx.doi.org/10.3390/math9141607.

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This paper deals with a method for the parameter and order identification of a fractional model. In contrast to existing approaches that can either handle noisy observations of the output signal or systems that are not at rest, the proposed method does not have to compromise between these two characteristics. To handle systems that are not at rest, the parameter, as well as the order identification, are based on the modulating function method. The novelty of the proposed method is that an optimization-based approach is used for the order identification. Thus, even if only noisy observations of

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