Zaloguj się

Gotowe bibliografie tematyczne / Deep Discriminative Probabilistic Models / Artykuły w czasopismach

Kliknij ten link, aby zobaczyć inne rodzaje publikacji na ten temat: Deep Discriminative Probabilistic Models.

Artykuły w czasopismach na temat „Deep Discriminative Probabilistic Models”

Autor: Grafiati

Data publikacji: 6 września 2023

Data aktualizacji: 7 września 2023

Utwórz poprawne odniesienie w stylach APA, MLA, Chicago, Harvard i wielu innych

Wybierz rodzaj źródła:

Sprawdź 50 najlepszych artykułów w czasopismach naukowych na temat „Deep Discriminative Probabilistic Models”.

Przycisk „Dodaj do bibliografii” jest dostępny obok każdej pracy w bibliografii. Użyj go – a my automatycznie utworzymy odniesienie bibliograficzne do wybranej pracy w stylu cytowania, którego potrzebujesz: APA, MLA, Harvard, Chicago, Vancouver itp.

Możesz również pobrać pełny tekst publikacji naukowej w formacie „.pdf” i przeczytać adnotację do pracy online, jeśli odpowiednie parametry są dostępne w metadanych.

Przeglądaj artykuły w czasopismach z różnych dziedzin i twórz odpowiednie bibliografie.

1

Kamran, Fahad, and Jenna Wiens. "Estimating Calibrated Individualized Survival Curves with Deep Learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 1 (2021): 240–48. http://dx.doi.org/10.1609/aaai.v35i1.16098.

Pełny tekst źródła

Streszczenie:

In survival analysis, deep learning approaches have been proposed for estimating an individual's probability of survival over some time horizon. Such approaches can capture complex non-linear relationships, without relying on restrictive assumptions regarding the relationship between an individual's characteristics and their underlying survival process. To date, however, these methods have focused primarily on optimizing discriminative performance and have ignored model calibration. Well-calibrated survival curves present realistic and meaningful probabilistic estimates of the true underlying

Style APA, Harvard, Vancouver, ISO itp.

2

Al Moubayed, Noura, Stephen McGough, and Bashar Awwad Shiekh Hasan. "Beyond the topics: how deep learning can improve the discriminability of probabilistic topic modelling." PeerJ Computer Science 6 (January 27, 2020): e252. http://dx.doi.org/10.7717/peerj-cs.252.

Pełny tekst źródła

Streszczenie:

The article presents a discriminative approach to complement the unsupervised probabilistic nature of topic modelling. The framework transforms the probabilities of the topics per document into class-dependent deep learning models that extract highly discriminatory features suitable for classification. The framework is then used for sentiment analysis with minimum feature engineering. The approach transforms the sentiment analysis problem from the word/document domain to the topics domain making it more robust to noise and incorporating complex contextual information that are not represented o

Style APA, Harvard, Vancouver, ISO itp.

3

Bhattacharya, Debswapna. "refineD: improved protein structure refinement using machine learning based restrained relaxation." Bioinformatics 35, no. 18 (2019): 3320–28. http://dx.doi.org/10.1093/bioinformatics/btz101.

Pełny tekst źródła

Streszczenie:

AbstractMotivationProtein structure refinement aims to bring moderately accurate template-based protein models closer to the native state through conformational sampling. However, guiding the sampling towards the native state by effectively using restraints remains a major issue in structure refinement.ResultsHere, we develop a machine learning based restrained relaxation protocol that uses deep discriminative learning based binary classifiers to predict multi-resolution probabilistic restraints from the starting structure and subsequently converts these restraints to be integrated into Rosett

Style APA, Harvard, Vancouver, ISO itp.

4

Wu, Boxi, Jie Jiang, Haidong Ren, et al. "Towards In-Distribution Compatible Out-of-Distribution Detection." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 9 (2023): 10333–41. http://dx.doi.org/10.1609/aaai.v37i9.26230.

Pełny tekst źródła

Streszczenie:

Deep neural network, despite its remarkable capability of discriminating targeted in-distribution samples, shows poor performance on detecting anomalous out-of-distribution data. To address this defect, state-of-the-art solutions choose to train deep networks on an auxiliary dataset of outliers. Various training criteria for these auxiliary outliers are proposed based on heuristic intuitions. However, we find that these intuitively designed outlier training criteria can hurt in-distribution learning and eventually lead to inferior performance. To this end, we identify three causes of the in-di

Style APA, Harvard, Vancouver, ISO itp.

5

Roy, Debaditya, Sarunas Girdzijauskas, and Serghei Socolovschi. "Confidence-Calibrated Human Activity Recognition." Sensors 21, no. 19 (2021): 6566. http://dx.doi.org/10.3390/s21196566.

Pełny tekst źródła

Streszczenie:

Wearable sensors are widely used in activity recognition (AR) tasks with broad applicability in health and well-being, sports, geriatric care, etc. Deep learning (DL) has been at the forefront of progress in activity classification with wearable sensors. However, most state-of-the-art DL models used for AR are trained to discriminate different activity classes at high accuracy, not considering the confidence calibration of predictive output of those models. This results in probabilistic estimates that might not capture the true likelihood and is thus unreliable. In practice, it tends to produc

Style APA, Harvard, Vancouver, ISO itp.

6

Tsuda, Koji, Motoaki Kawanabe, Gunnar Rätsch, Sören Sonnenburg, and Klaus-Robert Müller. "A New Discriminative Kernel from Probabilistic Models." Neural Computation 14, no. 10 (2002): 2397–414. http://dx.doi.org/10.1162/08997660260293274.

Pełny tekst źródła

Streszczenie:

Recently, Jaakkola and Haussler (1999) proposed a method for constructing kernel functions from probabilistic models. Their so-called Fisher kernel has been combined with discriminative classifiers such as support vector machines and applied successfully in, for example, DNA and protein analysis. Whereas the Fisher kernel is calculated from the marginal log-likelihood, we propose the TOP kernel derived from tangent vectors of posterior log-odds. Furthermore, we develop a theoretical framework on feature extractors from probabilistic models and use it for analyzing the TOP kernel. In experiment

Style APA, Harvard, Vancouver, ISO itp.

7

Ahmed, Nisar, and Mark Campbell. "On estimating simple probabilistic discriminative models with subclasses." Expert Systems with Applications 39, no. 7 (2012): 6659–64. http://dx.doi.org/10.1016/j.eswa.2011.12.042.

Pełny tekst źródła

Style APA, Harvard, Vancouver, ISO itp.

8

Du, Fang, Jiangshe Zhang, Junying Hu, and Rongrong Fei. "Discriminative multi-modal deep generative models." Knowledge-Based Systems 173 (June 2019): 74–82. http://dx.doi.org/10.1016/j.knosys.2019.02.023.

Pełny tekst źródła

Style APA, Harvard, Vancouver, ISO itp.

9

Che, Tong, Xiaofeng Liu, Site Li, et al. "Deep Verifier Networks: Verification of Deep Discriminative Models with Deep Generative Models." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 8 (2021): 7002–10. http://dx.doi.org/10.1609/aaai.v35i8.16862.

Pełny tekst źródła

Streszczenie:

AI Safety is a major concern in many deep learning applications such as autonomous driving. Given a trained deep learning model, an important natural problem is how to reliably verify the model's prediction. In this paper, we propose a novel framework --- deep verifier networks (DVN) to detect unreliable inputs or predictions of deep discriminative models, using separately trained deep generative models. Our proposed model is based on conditional variational auto-encoders with disentanglement constraints to separate the label information from the latent representation. We give both intuitive a

Style APA, Harvard, Vancouver, ISO itp.

10

Masegosa, Andrés R., Rafael Cabañas, Helge Langseth, Thomas D. Nielsen, and Antonio Salmerón. "Probabilistic Models with Deep Neural Networks." Entropy 23, no. 1 (2021): 117. http://dx.doi.org/10.3390/e23010117.

Pełny tekst źródła

Streszczenie:

Recent advances in statistical inference have significantly expanded the toolbox of probabilistic modeling. Historically, probabilistic modeling has been constrained to very restricted model classes, where exact or approximate probabilistic inference is feasible. However, developments in variational inference, a general form of approximate probabilistic inference that originated in statistical physics, have enabled probabilistic modeling to overcome these limitations: (i) Approximate probabilistic inference is now possible over a broad class of probabilistic models containing a large number of

Style APA, Harvard, Vancouver, ISO itp.

11

Jong Kyoung Kim and Seungjin Choi. "Probabilistic Models for Semisupervised Discriminative Motif Discovery in DNA Sequences." IEEE/ACM Transactions on Computational Biology and Bioinformatics 8, no. 5 (2011): 1309–17. http://dx.doi.org/10.1109/tcbb.2010.84.

Pełny tekst źródła

Style APA, Harvard, Vancouver, ISO itp.

12

Fang, Yi, Luo Si, and Aditya P. Mathur. "Discriminative probabilistic models for expert search in heterogeneous information sources." Information Retrieval 14, no. 2 (2010): 158–77. http://dx.doi.org/10.1007/s10791-010-9139-3.

Pełny tekst źródła

Style APA, Harvard, Vancouver, ISO itp.

13

Ahmed, Nisar, and Mark Campbell. "Variational Bayesian Learning of Probabilistic Discriminative Models With Latent Softmax Variables." IEEE Transactions on Signal Processing 59, no. 7 (2011): 3143–54. http://dx.doi.org/10.1109/tsp.2011.2144587.

Pełny tekst źródła

Style APA, Harvard, Vancouver, ISO itp.

14

Wu, Ying Nian, Ruiqi Gao, Tian Han, and Song-Chun Zhu. "A tale of three probabilistic families: Discriminative, descriptive, and generative models." Quarterly of Applied Mathematics 77, no. 2 (2018): 423–65. http://dx.doi.org/10.1090/qam/1528.

Pełny tekst źródła

Style APA, Harvard, Vancouver, ISO itp.

15

Qin, Huafeng, and Peng Wang. "Finger-Vein Verification Based on LSTM Recurrent Neural Networks." Applied Sciences 9, no. 8 (2019): 1687. http://dx.doi.org/10.3390/app9081687.

Pełny tekst źródła

Streszczenie:

Finger-vein biometrics has been extensively investigated for personal verification. A challenge is that the finger-vein acquisition is affected by many factors, which results in many ambiguous regions in the finger-vein image. Generally, the separability between vein and background is poor in such regions. Despite recent advances in finger-vein pattern segmentation, current solutions still lack the robustness to extract finger-vein features from raw images because they do not take into account the complex spatial dependencies of vein pattern. This paper proposes a deep learning model to extrac

Style APA, Harvard, Vancouver, ISO itp.

16

Villanueva Llerena, Julissa, and Denis Deratani Maua. "Efficient Predictive Uncertainty Estimators for Deep Probabilistic Models." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 10 (2020): 13740–41. http://dx.doi.org/10.1609/aaai.v34i10.7142.

Pełny tekst źródła

Streszczenie:

Deep Probabilistic Models (DPM) based on arithmetic circuits representation, such as Sum-Product Networks (SPN) and Probabilistic Sentential Decision Diagrams (PSDD), have shown competitive performance in several machine learning tasks with interesting properties (Poon and Domingos 2011; Kisa et al. 2014). Due to the high number of parameters and scarce data, DPMs can produce unreliable and overconfident inference. This research aims at increasing the robustness of predictive inference with DPMs by obtaining new estimators of the predictive uncertainty. This problem is not new and the literatu

Style APA, Harvard, Vancouver, ISO itp.

17

Chu, Joseph Lin, and Adam Krzyźak. "The Recognition Of Partially Occluded Objects with Support Vector Machines, Convolutional Neural Networks and Deep Belief Networks." Journal of Artificial Intelligence and Soft Computing Research 4, no. 1 (2014): 5–19. http://dx.doi.org/10.2478/jaiscr-2014-0021.

Pełny tekst źródła

Streszczenie:

Abstract Biologically inspired artificial neural networks have been widely used for machine learning tasks such as object recognition. Deep architectures, such as the Convolutional Neural Network, and the Deep Belief Network have recently been implemented successfully for object recognition tasks. We conduct experiments to test the hypothesis that certain primarily generative models such as the Deep Belief Network should perform better on the occluded object recognition task than purely discriminative models such as Convolutional Neural Networks and Support Vector Machines. When the generative

Style APA, Harvard, Vancouver, ISO itp.

18

Wang, Liwei, Xiong Li, Zhuowen Tu, and Jiaya Jia. "Discriminative Clustering via Generative Feature Mapping." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (2021): 1162–68. http://dx.doi.org/10.1609/aaai.v26i1.8305.

Pełny tekst źródła

Streszczenie:

Existing clustering methods can be roughly classified into two categories: generative and discriminative approaches. Generative clustering aims to explain the data and thus is adaptive to the underlying data distribution; discriminative clustering, on the other hand, emphasizes on finding partition boundaries. In this paper, we take the advantages of both models by coupling the two paradigms through feature mapping derived from linearizing Bayesian classifiers. Such the feature mapping strategy maps nonlinear boundaries of generative clustering to linear ones in the feature space where we expl

Style APA, Harvard, Vancouver, ISO itp.

19

Buscombe, Daniel, and Paul Grams. "Probabilistic Substrate Classification with Multispectral Acoustic Backscatter: A Comparison of Discriminative and Generative Models." Geosciences 8, no. 11 (2018): 395. http://dx.doi.org/10.3390/geosciences8110395.

Pełny tekst źródła

Streszczenie:

We propose a probabilistic graphical model for discriminative substrate characterization, to support geological and biological habitat mapping in aquatic environments. The model, called a fully-connected conditional random field (CRF), is demonstrated using multispectral and monospectral acoustic backscatter from heterogeneous seafloors in Patricia Bay, British Columbia, and Bedford Basin, Nova Scotia. Unlike previously proposed discriminative algorithms, the CRF model considers both the relative backscatter magnitudes of different substrates and their relative proximities. The model therefore

Style APA, Harvard, Vancouver, ISO itp.

20

Luo, You-Wei, Chuan-Xian Ren, Pengfei Ge, Ke-Kun Huang, and Yu-Feng Yu. "Unsupervised Domain Adaptation via Discriminative Manifold Embedding and Alignment." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 5029–36. http://dx.doi.org/10.1609/aaai.v34i04.5943.

Pełny tekst źródła

Streszczenie:

Unsupervised domain adaptation is effective in leveraging the rich information from the source domain to the unsupervised target domain. Though deep learning and adversarial strategy make an important breakthrough in the adaptability of features, there are two issues to be further explored. First, the hard-assigned pseudo labels on the target domain are risky to the intrinsic data structure. Second, the batch-wise training manner in deep learning limits the description of the global structure. In this paper, a Riemannian manifold learning framework is proposed to achieve transferability and di

Style APA, Harvard, Vancouver, ISO itp.

21

Karami, Mahdi, and Dale Schuurmans. "Deep Probabilistic Canonical Correlation Analysis." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 9 (2021): 8055–63. http://dx.doi.org/10.1609/aaai.v35i9.16982.

Pełny tekst źródła

Streszczenie:

We propose a deep generative framework for multi-view learning based on a probabilistic interpretation of canonical correlation analysis (CCA). The model combines a linear multi-view layer in the latent space with deep generative networks as observation models, to decompose the variability in multiple views into a shared latent representation that describes the common underlying sources of variation and a set of viewspecific components. To approximate the posterior distribution of the latent multi-view layer, an efficient variational inference procedure is developed based on the solution of pr

Style APA, Harvard, Vancouver, ISO itp.

22

Cui, Bo, Guyue Hu, and Shan Yu. "DeepCollaboration: Collaborative Generative and Discriminative Models for Class Incremental Learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 2 (2021): 1175–83. http://dx.doi.org/10.1609/aaai.v35i2.16204.

Pełny tekst źródła

Streszczenie:

An important challenge for neural networks is to learn incrementally, i.e., learn new classes without catastrophic forgetting. To overcome this problem, generative replay technique has been suggested, which can generate samples belonging to learned classes while learning new ones. However, such generative models usually suffer from increased distribution mismatch between the generated and original samples along the learning process. In this work, we propose DeepCollaboration (D-Collab), a collaborative framework of deep generative and discriminative models to solve this problem effectively. We

Style APA, Harvard, Vancouver, ISO itp.

23

Gordon, Jonathan, and José Miguel Hernández-Lobato. "Combining deep generative and discriminative models for Bayesian semi-supervised learning." Pattern Recognition 100 (April 2020): 107156. http://dx.doi.org/10.1016/j.patcog.2019.107156.

Pełny tekst źródła

Style APA, Harvard, Vancouver, ISO itp.

24

Bai, Wenjun, Changqin Quan, and Zhi-Wei Luo. "Improving Generative and Discriminative Modelling Performance by Implementing Learning Constraints in Encapsulated Variational Autoencoders." Applied Sciences 9, no. 12 (2019): 2551. http://dx.doi.org/10.3390/app9122551.

Pełny tekst źródła

Streszczenie:

Learning latent representations of observed data that can favour both discriminative and generative tasks remains a challenging task in artificial-intelligence (AI) research. Previous attempts that ranged from the convex binding of discriminative and generative models to the semisupervised learning paradigm could hardly yield optimal performance on both generative and discriminative tasks. To this end, in this research, we harness the power of two neuroscience-inspired learning constraints, that is, dependence minimisation and regularisation constraints, to improve generative and discriminativ

Style APA, Harvard, Vancouver, ISO itp.

25

Li, Fuqiang, Tongzhuang Zhang, Yong Liu, and Feiqi Long. "Deep Residual Vector Encoding for Vein Recognition." Electronics 11, no. 20 (2022): 3300. http://dx.doi.org/10.3390/electronics11203300.

Pełny tekst źródła

Streszczenie:

Vein recognition has been drawing more attention recently because it is highly secure and reliable for practical biometric applications. However, underlying issues such as uneven illumination, low contrast, and sparse patterns with high inter-class similarities make the traditional vein recognition systems based on hand-engineered features unreliable. Recent successes of convolutional neural networks (CNNs) for large-scale image recognition tasks motivate us to replace the traditional hand-engineered features with the superior CNN to design a robust and discriminative vein recognition system.

Style APA, Harvard, Vancouver, ISO itp.

26

Hu, Gang, Chahna Dixit, and Guanqiu Qi. "Discriminative Shape Feature Pooling in Deep Neural Networks." Journal of Imaging 8, no. 5 (2022): 118. http://dx.doi.org/10.3390/jimaging8050118.

Pełny tekst źródła

Streszczenie:

Although deep learning approaches are able to generate generic image features from massive labeled data, discriminative handcrafted features still have advantages in providing explicit domain knowledge and reflecting intuitive visual understanding. Much of the existing research focuses on integrating both handcrafted features and deep networks to leverage the benefits. However, the issues of parameter quality have not been effectively solved in existing applications of handcrafted features in deep networks. In this research, we propose a method that enriches deep network features by utilizing

Style APA, Harvard, Vancouver, ISO itp.

27

Coto-Jiménez, Marvin. "Discriminative Multi-Stream Postfilters Based on Deep Learning for Enhancing Statistical Parametric Speech Synthesis." Biomimetics 6, no. 1 (2021): 12. http://dx.doi.org/10.3390/biomimetics6010012.

Pełny tekst źródła

Streszczenie:

Statistical parametric speech synthesis based on Hidden Markov Models has been an important technique for the production of artificial voices, due to its ability to produce results with high intelligibility and sophisticated features such as voice conversion and accent modification with a small footprint, particularly for low-resource languages where deep learning-based techniques remain unexplored. Despite the progress, the quality of the results, mainly based on Hidden Markov Models (HMM) does not reach those of the predominant approaches, based on unit selection of speech segments of deep l

Style APA, Harvard, Vancouver, ISO itp.

28

Adedigba, Adeyinka P., Steve A. Adeshina, and Abiodun M. Aibinu. "Performance Evaluation of Deep Learning Models on Mammogram Classification Using Small Dataset." Bioengineering 9, no. 4 (2022): 161. http://dx.doi.org/10.3390/bioengineering9040161.

Pełny tekst źródła

Streszczenie:

Cancer is the second leading cause of death globally, and breast cancer (BC) is the second most reported cancer. Although the incidence rate is reducing in developed countries, the reverse is the case in low- and middle-income countries. Early detection has been found to contain cancer growth, prevent metastasis, ease treatment, and reduce mortality by 25%. The digital mammogram is one of the most common, cheapest, and most effective BC screening techniques capable of early detection of up to 90% BC incidence. However, the mammogram is one of the most difficult medical images to analyze. In th

Style APA, Harvard, Vancouver, ISO itp.

29

Alshazly, Hammam, Christoph Linse, Erhardt Barth, and Thomas Martinetz. "Ensembles of Deep Learning Models and Transfer Learning for Ear Recognition." Sensors 19, no. 19 (2019): 4139. http://dx.doi.org/10.3390/s19194139.

Pełny tekst źródła

Streszczenie:

The recognition performance of visual recognition systems is highly dependent on extracting and representing the discriminative characteristics of image data. Convolutional neural networks (CNNs) have shown unprecedented success in a variety of visual recognition tasks due to their capability to provide in-depth representations exploiting visual image features of appearance, color, and texture. This paper presents a novel system for ear recognition based on ensembles of deep CNN-based models and more specifically the Visual Geometry Group (VGG)-like network architectures for extracting discrim

Style APA, Harvard, Vancouver, ISO itp.

30

Maroñas, Juan, Roberto Paredes, and Daniel Ramos. "Calibration of deep probabilistic models with decoupled bayesian neural networks." Neurocomputing 407 (September 2020): 194–205. http://dx.doi.org/10.1016/j.neucom.2020.04.103.

Pełny tekst źródła

Style APA, Harvard, Vancouver, ISO itp.

31

Li, Zhenjun, Xi Liu, Dawei Kou, Yi Hu, Qingrui Zhang, and Qingxi Yuan. "Probabilistic Models for the Shear Strength of RC Deep Beams." Applied Sciences 13, no. 8 (2023): 4853. http://dx.doi.org/10.3390/app13084853.

Pełny tekst źródła

Streszczenie:

A new shear strength determination of reinforced concrete (RC) deep beams was proposed by using a statistical approach. The Bayesian–MCMC (Markov Chain Monte Carlo) method was introduced to establish a new shear prediction model and to improve seven existing deterministic models with a database of 645 experimental data. The bias correction terms of deterministic models were described by key explanatory terms identified by a systematic removal process. Considering multi-parameters, the Gibbs sampling was used to solve the high dimensional integration problem and to determine optimum and reliabl

Style APA, Harvard, Vancouver, ISO itp.

32

Liu, Shengyi. "Model Extraction Attack and Defense on Deep Generative Models." Journal of Physics: Conference Series 2189, no. 1 (2022): 012024. http://dx.doi.org/10.1088/1742-6596/2189/1/012024.

Pełny tekst źródła

Streszczenie:

Abstract The security issues of machine learning have aroused much attention and model extraction attack is one of them. The definition of model extraction attack is that an adversary can collect data through query access to a victim model and train a substitute model with it in order to steal the functionality of the target model. At present, most of the related work has focused on the research of model extraction attack against discriminative models while this paper pays attention to deep generative models. First, considering the difference of an adversary` goals, the attacks are taxonomized

Style APA, Harvard, Vancouver, ISO itp.

33

Bai, Shuang. "Scene Categorization Through Using Objects Represented by Deep Features." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 09 (2017): 1755013. http://dx.doi.org/10.1142/s0218001417550138.

Pełny tekst źródła

Streszczenie:

Objects in scenes are thought to be important for scene recognition. In this paper, we propose to utilize scene-specific objects represented by deep features for scene categorization. Our approach combines benefits of deep learning and Latent Support Vector Machine (LSVM) to train a set of scene-specific object models for each scene category. Specifically, we first use deep Convolutional Neural Networks (CNNs) pre-trained on the large-scale object-centric image database ImageNet to learn rich object features and a large number of general object concepts. Then, the pre-trained CNNs is adopted t

Style APA, Harvard, Vancouver, ISO itp.

34

Kumar, Parmod, D. Suganthi, K. Valarmathi, et al. "A Multi-Thresholding-Based Discriminative Neural Classifier for Detection of Retinoblastoma Using CNN Models." BioMed Research International 2023 (February 6, 2023): 1–9. http://dx.doi.org/10.1155/2023/5803661.

Pełny tekst źródła

Streszczenie:

Cancer is one of the vital diseases which lead to the uncontrollable growth of the cell, and it affects the body tissue. A type of cancer that affects the children below five years and adults in a rare case is called retinoblastoma. It affects the retina in the eye and the surrounding region of eye like the eyelid, and sometimes, it leads to vision loss if it is not diagnosed at the early stage. MRI and CT are widely used scanning procedures to identify the cancerous region in the eye. Current screening methods for cancer region identification needs the clinicians’ support to spot the affected

Style APA, Harvard, Vancouver, ISO itp.

35

Yu, Hee-Jin, Chang-Hwan Son, and Dong Hyuk Lee. "Apple Leaf Disease Identification Through Region-of-Interest-Aware Deep Convolutional Neural Network." Journal of Imaging Science and Technology 64, no. 2 (2020): 20507–1. http://dx.doi.org/10.2352/j.imagingsci.technol.2020.64.2.020507.

Pełny tekst źródła

Streszczenie:

Abstract Traditional approaches for the identification of leaf diseases involve the use of handcrafted features such as colors and textures for feature extraction. Therefore, these approaches may have limitations in extracting abundant and discriminative features. Although deep learning approaches have been recently introduced to overcome the shortcomings of traditional approaches, existing deep learning models such as VGG and ResNet have been used in these approaches. This indicates that the approach can be further improved to increase the discriminative power because the spatial attention me

Style APA, Harvard, Vancouver, ISO itp.

36

Boursin, Nicolas, Carl Remlinger, and Joseph Mikael. "Deep Generators on Commodity Markets Application to Deep Hedging." Risks 11, no. 1 (2022): 7. http://dx.doi.org/10.3390/risks11010007.

Pełny tekst źródła

Streszczenie:

Four deep generative methods for time series are studied on commodity markets and compared with classical probabilistic models. The lack of data in the case of deep hedgers is a common flaw, which deep generative methods seek to address. In the specific case of commodities, it turns out that these generators can also be used to refine the price models by tackling the high-dimensional challenges. In this work, the synthetic time series of commodity prices produced by such generators are studied and then used to train deep hedgers on various options. A fully data-driven approach to commodity ris

Style APA, Harvard, Vancouver, ISO itp.

37

D’Andrea, Fabio, Pierre Gentine, Alan K. Betts, and Benjamin R. Lintner. "Triggering Deep Convection with a Probabilistic Plume Model." Journal of the Atmospheric Sciences 71, no. 11 (2014): 3881–901. http://dx.doi.org/10.1175/jas-d-13-0340.1.

Pełny tekst źródła

Streszczenie:

Abstract A model unifying the representation of the planetary boundary layer and dry, shallow, and deep convection, the probabilistic plume model (PPM), is presented. Its capacity to reproduce the triggering of deep convection over land is analyzed in detail. The model accurately reproduces the timing of shallow convection and of deep convection onset over land, which is a major issue in many current general climate models. PPM is based on a distribution of plumes with varying thermodynamic states (potential temperature and specific humidity) induced by surface-layer turbulence. Precipitation

Style APA, Harvard, Vancouver, ISO itp.

38

Serpell, Cristián, Ignacio A. Araya, Carlos Valle, and Héctor Allende. "Addressing model uncertainty in probabilistic forecasting using Monte Carlo dropout." Intelligent Data Analysis 24 (December 4, 2020): 185–205. http://dx.doi.org/10.3233/ida-200015.

Pełny tekst źródła

Streszczenie:

In recent years, deep learning models have been developed to address probabilistic forecasting tasks, assuming an implicit stochastic process that relates past observed values to uncertain future values. These models are capable of capturing the inherent uncertainty of the underlying process, but they ignore the model uncertainty that comes from the fact of not having infinite data. This work proposes addressing the model uncertainty problem using Monte Carlo dropout, a variational approach that assigns distributions to the weights of a neural network instead of simply using fixed values. This

Style APA, Harvard, Vancouver, ISO itp.

39

Qian, Weizhu, Fabrice Lauri, and Franck Gechter. "Supervised and semi-supervised deep probabilistic models for indoor positioning problems." Neurocomputing 435 (May 2021): 228–38. http://dx.doi.org/10.1016/j.neucom.2020.12.131.

Pełny tekst źródła

Style APA, Harvard, Vancouver, ISO itp.

40

Wang, Wenzheng, Yuqi Han, Chenwei Deng, and Zhen Li. "Hyperspectral Image Classification via Deep Structure Dictionary Learning." Remote Sensing 14, no. 9 (2022): 2266. http://dx.doi.org/10.3390/rs14092266.

Pełny tekst źródła

Streszczenie:

The construction of diverse dictionaries for sparse representation of hyperspectral image (HSI) classification has been a hot topic over the past few years. However, compared with convolutional neural network (CNN) models, dictionary-based models cannot extract deeper spectral information, which will reduce their performance for HSI classification. Moreover, dictionary-based methods have low discriminative capability, which leads to less accurate classification. To solve the above problems, we propose a deep learning-based structure dictionary for HSI classification in this paper. The core ide

Style APA, Harvard, Vancouver, ISO itp.

41

Andrianomena, Sambatra. "Probabilistic learning for pulsar classification." Journal of Cosmology and Astroparticle Physics 2022, no. 10 (2022): 016. http://dx.doi.org/10.1088/1475-7516/2022/10/016.

Pełny tekst źródła

Streszczenie:

Abstract In this work, we explore the possibility of using probabilistic learning to identify pulsar candidates. We make use of Deep Gaussian Process (DGP) and Deep Kernel Learning (DKL). Trained on a balanced training set in order to avoid the effect of class imbalance, the performance of the models, achieving relatively high probability of differentiating the positive class from the negative one (roc-auc ∼ 0.98), is very promising overall. We estimate the predictive entropy of each model predictions and find that DKL is more confident than DGP in its predictions and provides better uncertain

Style APA, Harvard, Vancouver, ISO itp.

42

Kim, Hyesuk, and Incheol Kim. "Dynamic Arm Gesture Recognition Using Spherical Angle Features and Hidden Markov Models." Advances in Human-Computer Interaction 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/785349.

Pełny tekst źródła

Streszczenie:

We introduce a vision-based arm gesture recognition (AGR) system using Kinect. The AGR system learns the discrete Hidden Markov Model (HMM), an effective probabilistic graph model for gesture recognition, from the dynamic pose of the arm joints provided by the Kinect API. Because Kinect’s viewpoint and the subject’s arm length can substantially affect the estimated 3D pose of each joint, it is difficult to recognize gestures reliably with these features. The proposed system performs the feature transformation that changes the 3D Cartesian coordinates of each joint into the 2D spherical angles

Style APA, Harvard, Vancouver, ISO itp.

43

Murad, Abdulmajid, Frank Alexander Kraemer, Kerstin Bach, and Gavin Taylor. "Probabilistic Deep Learning to Quantify Uncertainty in Air Quality Forecasting." Sensors 21, no. 23 (2021): 8009. http://dx.doi.org/10.3390/s21238009.

Pełny tekst źródła

Streszczenie:

Data-driven forecasts of air quality have recently achieved more accurate short-term predictions. However, despite their success, most of the current data-driven solutions lack proper quantifications of model uncertainty that communicate how much to trust the forecasts. Recently, several practical tools to estimate uncertainty have been developed in probabilistic deep learning. However, there have not been empirical applications and extensive comparisons of these tools in the domain of air quality forecasts. Therefore, this work applies state-of-the-art techniques of uncertainty quantification

Style APA, Harvard, Vancouver, ISO itp.

44

Adams, Jadie. "Probabilistic Shape Models of Anatomy Directly from Images." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 13 (2023): 16107–8. http://dx.doi.org/10.1609/aaai.v37i13.26914.

Pełny tekst źródła

Streszczenie:

Statistical shape modeling (SSM) is an enabling tool in medical image analysis as it allows for population-based quantitative analysis. The traditional pipeline for landmark-based SSM from images requires painstaking and cost-prohibitive steps. My thesis aims to leverage probabilistic deep learning frameworks to streamline the adoption of SSM in biomedical research and practice. The expected outcomes of this work will be new frameworks for SSM that (1) provide reliable and calibrated uncertainty quantification, (2) are effective given limited or sparsely annotated/incomplete data, and (3) can

Style APA, Harvard, Vancouver, ISO itp.

45

Ravuri, Suman, Karel Lenc, Matthew Willson, et al. "Skilful precipitation nowcasting using deep generative models of radar." Nature 597, no. 7878 (2021): 672–77. http://dx.doi.org/10.1038/s41586-021-03854-z.

Pełny tekst źródła

Streszczenie:

AbstractPrecipitation nowcasting, the high-resolution forecasting of precipitation up to two hours ahead, supports the real-world socioeconomic needs of many sectors reliant on weather-dependent decision-making1,2. State-of-the-art operational nowcasting methods typically advect precipitation fields with radar-based wind estimates, and struggle to capture important non-linear events such as convective initiations3,4. Recently introduced deep learning methods use radar to directly predict future rain rates, free of physical constraints5,6. While they accurately predict low-intensity rainfall, t

Style APA, Harvard, Vancouver, ISO itp.

46

Huang, Jiabo, Qi Dong, Shaogang Gong, and Xiatian Zhu. "Unsupervised Deep Learning via Affinity Diffusion." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 11029–36. http://dx.doi.org/10.1609/aaai.v34i07.6757.

Pełny tekst źródła

Streszczenie:

Convolutional neural networks (CNNs) have achieved unprecedented success in a variety of computer vision tasks. However, they usually rely on supervised model learning with the need for massive labelled training data, limiting dramatically their usability and deployability in real-world scenarios without any labelling budget. In this work, we introduce a general-purpose unsupervised deep learning approach to deriving discriminative feature representations. It is based on self-discovering semantically consistent groups of unlabelled training samples with the same class concepts through a progre

Style APA, Harvard, Vancouver, ISO itp.

47

Collins, Michael, and Terry Koo. "Discriminative Reranking for Natural Language Parsing." Computational Linguistics 31, no. 1 (2005): 25–70. http://dx.doi.org/10.1162/0891201053630273.

Pełny tekst źródła

Streszczenie:

This article considers approaches which rerank the output of an existing probabilistic parser. The base parser produces a set of candidate parses for each input sentence, with associated probabilities that define an initial ranking of these parses. A second model then attempts to improve upon this initial ranking, using additional features of the tree as evidence. The strength of our approach is that it allows a tree to be represented as an arbitrary set of features, without concerns about how these features interact or overlap and without the need to define a derivation or a generative model

Style APA, Harvard, Vancouver, ISO itp.

48

Mashlakov, Aleksei, Toni Kuronen, Lasse Lensu, Arto Kaarna, and Samuli Honkapuro. "Assessing the performance of deep learning models for multivariate probabilistic energy forecasting." Applied Energy 285 (March 2021): 116405. http://dx.doi.org/10.1016/j.apenergy.2020.116405.

Pełny tekst źródła

Style APA, Harvard, Vancouver, ISO itp.

49

Duan, Yun. "A Novel Interval Energy-Forecasting Method for Sustainable Building Management Based on Deep Learning." Sustainability 14, no. 14 (2022): 8584. http://dx.doi.org/10.3390/su14148584.

Pełny tekst źródła

Streszczenie:

Energy conservation in buildings has increasingly become a hot issue for the Chinese government. Compared to deterministic load prediction, probabilistic load forecasting is more suitable for long-term planning and management of building energy consumption. In this study, we propose a probabilistic load-forecasting method for daily and weekly indoor load. The methodology is based on the long short-term memory (LSTM) model and penalized quantile regression (PQR). A comprehensive analysis for a time period of a year is conducted using the proposed method, and back propagation neural networks (BP

Style APA, Harvard, Vancouver, ISO itp.

50

Krogh, Anders, and Søren Kamaric Riis. "Hidden Neural Networks." Neural Computation 11, no. 2 (1999): 541–63. http://dx.doi.org/10.1162/089976699300016764.

Pełny tekst źródła

Streszczenie:

A general framework for hybrids of hidden Markov models (HMMs) and neural networks (NNs) called hidden neural networks (HNNs) is described. The article begins by reviewing standard HMMs and estimation by conditional maximum likelihood, which is used by the HNN. In the HNN, the usual HMM probability parameters are replaced by the outputs of state-specific neural networks. As opposed to many other hybrids, the HNN is normalized globally and therefore has a valid probabilistic interpretation. All parameters in the HNN are estimated simultaneously according to the discriminative conditional maximu

Style APA, Harvard, Vancouver, ISO itp.

Oferujemy zniżki na wszystkie plany premium dla autorów, których prace zostały uwzględnione w tematycznych zestawieniach literatury. Skontaktuj się z nami, aby uzyskać unikalny kod promocyjny!