Relevant bibliographies by topics / Deep Discriminative Probabilistic Models

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Academic literature on the topic 'Deep Discriminative Probabilistic Models'

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Published: 6 September 2023
Last updated: 7 September 2023

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Journal articles on the topic "Deep Discriminative Probabilistic Models"

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Kamran, Fahad, and Jenna Wiens. "Estimating Calibrated Individualized Survival Curves with Deep Learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 1 (May 18, 2021): 240–48. http://dx.doi.org/10.1609/aaai.v35i1.16098.

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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 survival process for an individual. However, due to the lack of ground-truth regarding the underlying stochastic process of survival for an individual, optimizing and measuring calibration in survival analysis is an inherently difficult task. In this work, we i) highlight the shortcomings of existing approaches in terms of calibration and ii) propose a new training scheme for optimizing deep survival analysis models that maximizes discriminative performance, subject to good calibration. Compared to state-of-the-art approaches across two publicly available datasets, our proposed training scheme leads to significant improvements in calibration, while maintaining good discriminative performance.
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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.

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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 otherwise. A stacked denoising autoencoder (SDA) is then used to model the complex relationship among the topics per sentiment with minimum assumptions. To achieve this, a distinct topic model and SDA per sentiment polarity is built with an additional decision layer for classification. The framework is tested on a comprehensive collection of benchmark datasets that vary in sample size, class bias and classification task. A significant improvement to the state of the art is achieved without the need for a sentiment lexica or over-engineered features. A further analysis is carried out to explain the observed improvement in accuracy.
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Bhattacharya, Debswapna. "refineD: improved protein structure refinement using machine learning based restrained relaxation." Bioinformatics 35, no. 18 (February 13, 2019): 3320–28. http://dx.doi.org/10.1093/bioinformatics/btz101.

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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 Rosetta all-atom energy function as additional scoring terms during structure refinement. We use four restraint resolutions as adopted in GDT-HA (0.5, 1, 2 and 4 Å), centered on the Cα atom of each residue that are predicted by ensemble of four deep discriminative classifiers trained using combinations of sequence and structure-derived features as well as several energy terms from Rosetta centroid scoring function. The proposed method, refineD, has been found to produce consistent and substantial structural refinement through the use of cumulative and non-cumulative restraints on 150 benchmarking targets. refineD outperforms unrestrained relaxation strategy or relaxation that is restrained to starting structures using the FastRelax application of Rosetta or atomic-level energy minimization based ModRefiner method as well as molecular dynamics (MD) simulation based FG-MD protocol. Furthermore, by adjusting restraint resolutions, the method addresses the tradeoff that exists between degree and consistency of refinement. These results demonstrate a promising new avenue for improving accuracy of template-based protein models by effectively guiding conformational sampling during structure refinement through the use of machine learning based restraints.Availability and implementationhttp://watson.cse.eng.auburn.edu/refineD/.Supplementary informationSupplementary data are available at Bioinformatics online.
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Wu, Boxi, Jie Jiang, Haidong Ren, Zifan Du, Wenxiao Wang, Zhifeng Li, Deng Cai, Xiaofei He, Binbin Lin, and Wei Liu. "Towards In-Distribution Compatible Out-of-Distribution Detection." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 9 (June 26, 2023): 10333–41. http://dx.doi.org/10.1609/aaai.v37i9.26230.

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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-distribution incompatibility: contradictory gradient, false likelihood, and distribution shift. Based on our new understandings, we propose a new out-of-distribution detection method by adapting both the top-design of deep models and the loss function. Our method achieves in-distribution compatibility by pursuing less interference with the probabilistic characteristic of in-distribution features. On several benchmarks, our method not only achieves the state-of-the-art out-of-distribution detection performance but also improves the in-distribution accuracy.
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Roy, Debaditya, Sarunas Girdzijauskas, and Serghei Socolovschi. "Confidence-Calibrated Human Activity Recognition." Sensors 21, no. 19 (September 30, 2021): 6566. http://dx.doi.org/10.3390/s21196566.

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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 produce overconfident estimates. In this paper, the problem is addressed by proposing deep time ensembles, a novel ensembling method capable of producing calibrated confidence estimates from neural network architectures. In particular, the method trains an ensemble of network models with temporal sequences extracted by varying the window size over the input time series and averaging the predictive output. The method is evaluated on four different benchmark HAR datasets and three different neural network architectures. Across all the datasets and architectures, our method shows an improvement in calibration by reducing the expected calibration error (ECE)by at least 40%, thereby providing superior likelihood estimates. In addition to providing reliable predictions our method also outperforms the state-of-the-art classification results in the WISDM, UCI HAR, and PAMAP2 datasets and performs as good as the state-of-the-art in the Skoda dataset.
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Tsuda, Koji, Motoaki Kawanabe, Gunnar Rätsch, Sören Sonnenburg, and Klaus-Robert Müller. "A New Discriminative Kernel from Probabilistic Models." Neural Computation 14, no. 10 (October 1, 2002): 2397–414. http://dx.doi.org/10.1162/08997660260293274.

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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 experiments, our new discriminative TOP kernel compares favorably to the Fisher kernel.
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Ahmed, Nisar, and Mark Campbell. "On estimating simple probabilistic discriminative models with subclasses." Expert Systems with Applications 39, no. 7 (June 2012): 6659–64. http://dx.doi.org/10.1016/j.eswa.2011.12.042.

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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.

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Che, Tong, Xiaofeng Liu, Site Li, Yubin Ge, Ruixiang Zhang, Caiming Xiong, and Yoshua Bengio. "Deep Verifier Networks: Verification of Deep Discriminative Models with Deep Generative Models." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 8 (May 18, 2021): 7002–10. http://dx.doi.org/10.1609/aaai.v35i8.16862.

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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 and theoretical justifications for the model. Our verifier network is trained independently with the prediction model, which eliminates the need of retraining the verifier network for a new model. We test the verifier network on both out-of-distribution detection and adversarial example detection problems, as well as anomaly detection problems in structured prediction tasks such as image caption generation. We achieve state-of-the-art results in all of these problems.
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Masegosa, Andrés R., Rafael Cabañas, Helge Langseth, Thomas D. Nielsen, and Antonio Salmerón. "Probabilistic Models with Deep Neural Networks." Entropy 23, no. 1 (January 18, 2021): 117. http://dx.doi.org/10.3390/e23010117.

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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 parameters, and (ii) scalable inference methods based on stochastic gradient descent and distributed computing engines allow probabilistic modeling to be applied to massive data sets. One important practical consequence of these advances is the possibility to include deep neural networks within probabilistic models, thereby capturing complex non-linear stochastic relationships between the random variables. These advances, in conjunction with the release of novel probabilistic modeling toolboxes, have greatly expanded the scope of applications of probabilistic models, and allowed the models to take advantage of the recent strides made by the deep learning community. In this paper, we provide an overview of the main concepts, methods, and tools needed to use deep neural networks within a probabilistic modeling framework.
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Dissertations / Theses on the topic "Deep Discriminative Probabilistic Models"

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Misino, Eleonora. "Deep Generative Models with Probabilistic Logic Priors." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24058/.

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Many different extensions of the VAE framework have been introduced in the past. How­ ever, the vast majority of them focused on pure sub­-symbolic approaches that are not sufficient for solving generative tasks that require a form of reasoning. In this thesis, we propose the probabilistic logic VAE (PLVAE), a neuro-­symbolic deep generative model that combines the representational power of VAEs with the reasoning ability of probabilistic ­logic programming. The strength of PLVAE resides in its probabilistic ­logic prior, which provides an interpretable structure to the latent space that can be easily changed in order to apply the model to different scenarios. We provide empirical results of our approach by training PLVAE on a base task and then using the same model to generalize to novel tasks that involve reasoning with the same set of symbols.
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Zhai, Menghua. "Deep Probabilistic Models for Camera Geo-Calibration." UKnowledge, 2018. https://uknowledge.uky.edu/cs_etds/74.

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The ultimate goal of image understanding is to transfer visual images into numerical or symbolic descriptions of the scene that are helpful for decision making. Knowing when, where, and in which direction a picture was taken, the task of geo-calibration makes it possible to use imagery to understand the world and how it changes in time. Current models for geo-calibration are mostly deterministic, which in many cases fails to model the inherent uncertainties when the image content is ambiguous. Furthermore, without a proper modeling of the uncertainty, subsequent processing can yield overly confident predictions. To address these limitations, we propose a probabilistic model for camera geo-calibration using deep neural networks. While our primary contribution is geo-calibration, we also show that learning to geo-calibrate a camera allows us to implicitly learn to understand the content of the scene.
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Georgatzis, Konstantinos. "Dynamical probabilistic graphical models applied to physiological condition monitoring." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/28838.

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Intensive Care Units (ICUs) host patients in critical condition who are being monitored by sensors which measure their vital signs. These vital signs carry information about a patient’s physiology and can have a very rich structure at fine resolution levels. The task of analysing these biosignals for the purposes of monitoring a patient’s physiology is referred to as physiological condition monitoring. Physiological condition monitoring of patients in ICUs is of critical importance as their health is subject to a number of events of interest. For the purposes of this thesis, the overall task of physiological condition monitoring is decomposed into the sub-tasks of modelling a patient’s physiology a) under the effect of physiological or artifactual events and b) under the effect of drug administration. The first sub-task is concerned with modelling artifact (such as the taking of blood samples, suction events etc.), and physiological episodes (such as bradycardia), while the second sub-task is focussed on modelling the effect of drug administration on a patient’s physiology. The first contribution of this thesis is the formulation, development and validation of the Discriminative Switching Linear Dynamical System (DSLDS) for the first sub-task. The DSLDS is a discriminative model which identifies the state-of-health of a patient given their observed vital signs using a discriminative probabilistic classifier, and then infers their underlying physiological values conditioned on this status. It is demonstrated on two real-world datasets that the DSLDS is able to outperform an alternative, generative approach in most cases of interest, and that an a-mixture of the two models achieves higher performance than either of the two models separately. The second contribution of this thesis is the formulation, development and validation of the Input-Output Non-Linear Dynamical System (IO-NLDS) for the second sub-task. The IO-NLDS is a non-linear dynamical system for modelling the effect of drug infusions on the vital signs of patients. More specifically, in this thesis the focus is on modelling the effect of the widely used anaesthetic drug Propofol on a patient’s monitored depth of anaesthesia and haemodynamics. A comparison of the IO-NLDS with a model derived from the Pharmacokinetics/Pharmacodynamics (PK/PD) literature on a real-world dataset shows that significant improvements in predictive performance can be provided without requiring the incorporation of expert physiological knowledge.
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Wu, Di. "Human action recognition using deep probabilistic graphical models." Thesis, University of Sheffield, 2014. http://etheses.whiterose.ac.uk/6603/.

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Building intelligent systems that are capable of representing or extracting high-level representations from high-dimensional sensory data lies at the core of solving many A.I. related tasks. Human action recognition is an important topic in computer vision that lies in high-dimensional space. Its applications include robotics, video surveillance, human-computer interaction, user interface design, and multi-media video retrieval amongst others. A number of approaches have been proposed to extract representative features from high-dimensional temporal data, most commonly hard wired geometric or bio-inspired shape context features. This thesis first demonstrates some \emph{ad-hoc} hand-crafted rules for effectively encoding motion features, and later elicits a more generic approach for incorporating structured feature learning and reasoning, \ie deep probabilistic graphical models. The hierarchial dynamic framework first extracts high level features and then uses the learned representation for estimating emission probability to infer action sequences. We show that better action recognition can be achieved by replacing gaussian mixture models by Deep Neural Networks that contain many layers of features to predict probability distributions over states of Markov Models. The framework can be easily extended to include an ergodic state to segment and recognise actions simultaneously. The first part of the thesis focuses on analysis and applications of hand-crafted features for human action representation and classification. We show that the ``hard coded" concept of correlogram can incorporate correlations between time domain sequences and we further investigate multi-modal inputs, \eg depth sensor input and its unique traits for action recognition. The second part of this thesis focuses on marrying probabilistic graphical models with Deep Neural Networks (both Deep Belief Networks and Deep 3D Convolutional Neural Networks) for structured sequence prediction. The proposed Deep Dynamic Neural Network exhibits its general framework for structured 2D data representation and classification. This inspires us to further investigate for applying various graphical models for time-variant video sequences.
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Sokolovska, Nataliya. "Contributions to the estimation of probabilistic discriminative models: semi-supervised learning and feature selection." Phd thesis, Télécom ParisTech, 2010. http://pastel.archives-ouvertes.fr/pastel-00006257.

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Dans cette thèse nous étudions l'estimation de modèles probabilistes discriminants, surtout des aspects d'apprentissage semi-supervisé et de sélection de caractéristiques. Le but de l'apprentissage semi-supervisé est d'améliorer l'efficacité de l'apprentissage supervisé en utilisant des données non-étiquetées. Cet objectif est difficile à atteindre dans les cas des modèles discriminants. Les modèles probabilistes discriminants permettent de manipuler des représentations linguistiques riches, sous la forme de vecteurs de caractéristiques de très grande taille. Travailler en grande dimension pose des problèmes, en particulier computationnels, qui sont exacerbés dans le cadre de modèles de séquences tels que les champs aléatoires conditionnels (CRF). Notre contribution est double. Nous introduisons une méthode originale et simple pour intégrer des données non étiquetées dans une fonction objectif semi-supervisée. Nous démontrons alors que l'estimateur semi-supervisé correspondant est asymptotiquement optimal. Le cas de la régression logistique est illustré par des résultats d'expèriences. Dans cette étude, nous proposons un algorithme d'estimation pour les CRF qui réalise une sélection de modèle, par le truchement d'une pénalisation $L_1$. Nous présentons également les résultats d'expériences menées sur des tâches de traitement des langues (le chunking et la détection des entités nommées), en analysant les performances en généralisation et les caractéristiques sélectionnées. Nous proposons finalement diverses pistes pour améliorer l'efficacité computationelle de cette technique.
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Hager, Paul Andrew. "Investigation of connection between deep learning and probabilistic graphical models." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/119552.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (page 21).
The field of machine learning (ML) has benefitted greatly from its relationship with the field of classical statistics. In support of that continued expansion, the following proposes an alternative perspective at the link between these fields. The link focuses on probabilistic graphical models in the context of reinforcement learning. Viewing certain algorithms as reinforcement learning gives one an ability to map ML concepts to statistics problems. Training a multi-layer nonlinear perceptron algorithm is equivalent to structure learning problems in probabilistic graphical models (PGMs). The technique of boosting weak rules into an ensemble is weighted sampling. Finally regularizing neural networks using the dropout technique is conditioning on certain observations in PGMs.
by Paul Andrew Hager.
M. Eng.
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Azizpour, Hossein. "Visual Representations and Models: From Latent SVM to Deep Learning." Doctoral thesis, KTH, Datorseende och robotik, CVAP, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-192289.

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Two important components of a visual recognition system are representation and model. Both involves the selection and learning of the features that are indicative for recognition and discarding those features that are uninformative. This thesis, in its general form, proposes different techniques within the frameworks of two learning systems for representation and modeling. Namely, latent support vector machines (latent SVMs) and deep learning. First, we propose various approaches to group the positive samples into clusters of visually similar instances. Given a fixed representation, the sampled space of the positive distribution is usually structured. The proposed clustering techniques include a novel similarity measure based on exemplar learning, an approach for using additional annotation, and augmenting latent SVM to automatically find clusters whose members can be reliably distinguished from background class.  In another effort, a strongly supervised DPM is suggested to study how these models can benefit from privileged information. The extra information comes in the form of semantic parts annotation (i.e. their presence and location). And they are used to constrain DPMs latent variables during or prior to the optimization of the latent SVM. Its effectiveness is demonstrated on the task of animal detection. Finally, we generalize the formulation of discriminative latent variable models, including DPMs, to incorporate new set of latent variables representing the structure or properties of negative samples. Thus, we term them as negative latent variables. We show this generalization affects state-of-the-art techniques and helps the visual recognition by explicitly searching for counter evidences of an object presence. Following the resurgence of deep networks, in the last works of this thesis we have focused on deep learning in order to produce a generic representation for visual recognition. A Convolutional Network (ConvNet) is trained on a largely annotated image classification dataset called ImageNet with $\sim1.3$ million images. Then, the activations at each layer of the trained ConvNet can be treated as the representation of an input image. We show that such a representation is surprisingly effective for various recognition tasks, making it clearly superior to all the handcrafted features previously used in visual recognition (such as HOG in our first works on DPM). We further investigate the ways that one can improve this representation for a task in mind. We propose various factors involving before or after the training of the representation which can improve the efficacy of the ConvNet representation. These factors are analyzed on 16 datasets from various subfields of visual recognition.

QC 20160908

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Farouni, Tarek. "An Overview of Probabilistic Latent Variable Models with anApplication to the Deep Unsupervised Learning of ChromatinStates." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1492189894812539.

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Qian, Weizhu. "Discovering human mobility from mobile data : probabilistic models and learning algorithms." Thesis, Bourgogne Franche-Comté, 2020. http://www.theses.fr/2020UBFCA025.

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Les données d'utilisation des smartphones peuvent être utilisées pour étudier la mobilité humaine que ce soit en environnement extérieur ouvert ou à l'intérieur de bâtiments. Dans ce travail, nous étudions ces deux aspects de la mobilité humaine en proposant des algorithmes de machine learning adapté aux sources d'information disponibles dans chacun des contextes.Pour l'étude de la mobilité en environnement extérieur, nous utilisons les données de coordonnées GPS collectées pour découvrir les schémas de mobilité quotidiens des utilisateurs. Pour cela, nous proposons un algorithme de clustering automatique utilisant le Dirichlet process Gaussian mixture model (DPGMM) afin de regrouper les trajectoires GPS quotidiennes. Cette méthode de segmentation est basée sur l'estimation des densités de probabilité des trajectoires, ce qui atténue les problèmes causés par le bruit des données.Concernant l'étude de la mobilité humaine dans les bâtiments, nous utilisons les données d'empreintes digitales WiFi collectées par les smartphones. Afin de prédire la trajectoire d'un individu à l'intérieur d'un bâtiment, nous avons conçu un modèle hybride d'apprentissage profond, appelé convolutional mixture density recurrent neural network (CMDRNN), qui combine les avantages de différents réseaux de neurones profonds multiples. De plus, en ce qui concerne la localisation précise en intérieur, nous supposons qu'il existe une distribution latente régissant l'entrée et la sortie en même temps. Sur la base de cette hypothèse, nous avons développé un modèle d'apprentissage semi-supervisé basé sur le variational autoencoder (VAE). Dans la procédure d'apprentissage non supervisé, nous utilisons un modèle VAE pour apprendre une distribution latente de l'entrée qui est composée de données d'empreintes digitales WiFi. Dans la procédure d'apprentissage supervisé, nous utilisons un réseau de neurones pour calculer la cible, coordonnées par l'utilisateur. De plus, sur la base de la même hypothèse utilisée dans le modèle d'apprentissage semi-supervisé basé sur le VAE, nous exploitons la théorie des goulots d'étranglement de l'information pour concevoir un modèle basé sur le variational information bottleneck (VIB). Il s'agit d'un modèle d'apprentissage en profondeur de bout en bout plus facile à former et offrant de meilleures performances.Enfin, les méthodes proposées ont été validées sur plusieurs jeux de données publics acquis en situation réelle. Les résultats obtenus ont permis de vérifier l'efficacité de nos méthodes par rapport à l'existant
Smartphone usage data can be used to study human indoor and outdoor mobility. In our work, we investigate both aspects in proposing machine learning-based algorithms adapted to the different information sources that can be collected.In terms of outdoor mobility, we use the collected GPS coordinate data to discover the daily mobility patterns of the users. To this end, we propose an automatic clustering algorithm using the Dirichlet process Gaussian mixture model (DPGMM) so as to cluster the daily GPS trajectories. This clustering method is based on estimating probability densities of the trajectories, which alleviate the problems caused by the data noise.By contrast, we utilize the collected WiFi fingerprint data to study indoor human mobility. In order to predict the indoor user location at the next time points, we devise a hybrid deep learning model, called the convolutional mixture density recurrent neural network (CMDRNN), which combines the advantages of different multiple deep neural networks. Moreover, as for accurate indoor location recognition, we presume that there exists a latent distribution governing the input and output at the same time. Based on this assumption, we develop a variational auto-encoder (VAE)-based semi-supervised learning model. In the unsupervised learning procedure, we employ a VAE model to learn a latent distribution of the input, the WiFi fingerprint data. In the supervised learning procedure, we use a neural network to compute the target, the user coordinates. Furthermore, based on the same assumption used in the VAE-based semi-supervised learning model, we leverage the information bottleneck theory to devise a variational information bottleneck (VIB)-based model. This is an end-to-end deep learning model which is easier to train and has better performance.Finally, we validate thees proposed methods on several public real-world datasets providing thus results that verify the efficiencies of our methods as compared to other existing methods generally used
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SYED, MUHAMMAD FARRUKH SHAHID. "Data-Driven Approach based on Deep Learning and Probabilistic Models for PHY-Layer Security in AI-enabled Cognitive Radio IoT." Doctoral thesis, Università degli studi di Genova, 2021. http://hdl.handle.net/11567/1048543.

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Cognitive Radio Internet of Things (CR-IoT) has revolutionized almost every field of life and reshaped the technological world. Several tiny devices are seamlessly connected in a CR-IoT network to perform various tasks in many applications. Nevertheless, CR-IoT surfers from malicious attacks that pulverize communication and perturb network performance. Therefore, recently it is envisaged to introduce higher-level Artificial Intelligence (AI) by incorporating Self-Awareness (SA) capabilities into CR-IoT objects to facilitate CR-IoT networks to establish secure transmission against vicious attacks autonomously. In this context, sub-band information from the Orthogonal Frequency Division Multiplexing (OFDM) modulated transmission in the spectrum has been extracted from the radio device receiver terminal, and a generalized state vector (GS) is formed containing low dimension in-phase and quadrature components. Accordingly, a probabilistic method based on learning a switching Dynamic Bayesian Network (DBN) from OFDM transmission with no abnormalities has been proposed to statistically model signal behaviors inside the CR-IoT spectrum. A Bayesian filter, Markov Jump Particle Filter (MJPF), is implemented to perform state estimation and capture malicious attacks. Subsequently, GS containing a higher number of subcarriers has been investigated. In this connection, Variational autoencoders (VAE) is used as a deep learning technique to extract features from high dimension radio signals into low dimension latent space z, and DBN is learned based on GS containing latent space data. Afterward, to perform state estimation and capture abnormalities in a spectrum, Adapted-Markov Jump Particle Filter (A-MJPF) is deployed. The proposed method can capture anomaly that appears due to either jammer attacks in transmission or cognitive devices in a network experiencing different transmission sources that have not been observed previously. The performance is assessed using the receiver operating characteristic (ROC) curves and the area under the curve (AUC) metrics.
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Books on the topic "Deep Discriminative Probabilistic Models"

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A Discriminative Approach to Bayesian Filtering with Applications to Human Neural Decoding. Providence, USA: Brown University, 2019.

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Oaksford, Mike, and Nick Chater. Causal Models and Conditional Reasoning. Edited by Michael R. Waldmann. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199399550.013.5.

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There are deep intuitions that the meaning of conditional statements relate to probabilistic law-like dependencies. In this chapter it is argued that these intuitions can be captured by representing conditionals in causal Bayes nets (CBNs) and that this conjecture is theoretically productive. This proposal is borne out in a variety of results. First, causal considerations can provide a unified account of abstract and causal conditional reasoning. Second, a recent model (Fernbach & Erb, 2013) can be extended to the explicit causal conditional reasoning paradigm (Byrne, 1989), making some novel predictions on the way. Third, when embedded in the broader cognitive system involved in reasoning, causal model theory can provide a novel explanation for apparent violations of the Markov condition in causal conditional reasoning (Ali et al, 2011). Alternative explanations are also considered (see, Rehder, 2014a) with respect to this evidence. While further work is required, the chapter concludes that the conjecture that conditional reasoning is underpinned by representations and processes similar to CBNs is indeed a productive line of research.
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Trappenberg, Thomas P. Fundamentals of Machine Learning. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198828044.001.0001.

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Machine learning is exploding, both in research and for industrial applications. This book aims to be a brief introduction to this area given the importance of this topic in many disciplines, from sciences to engineering, and even for its broader impact on our society. This book tries to contribute with a style that keeps a balance between brevity of explanations, the rigor of mathematical arguments, and outlining principle ideas. At the same time, this book tries to give some comprehensive overview of a variety of methods to see their relation on specialization within this area. This includes some introduction to Bayesian approaches to modeling as well as deep learning. Writing small programs to apply machine learning techniques is made easy today by the availability of high-level programming systems. This book offers examples in Python with the machine learning libraries sklearn and Keras. The first four chapters concentrate largely on the practical side of applying machine learning techniques. The book then discusses more fundamental concepts and includes their formulation in a probabilistic context. This is followed by chapters on advanced models, that of recurrent neural networks and that of reinforcement learning. The book closes with a brief discussion on the impact of machine learning and AI on our society.
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Book chapters on the topic "Deep Discriminative Probabilistic Models"

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Sucar, Luis Enrique. "Deep Learning and Graphical Models." In Probabilistic Graphical Models, 327–46. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61943-5_16.

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Gustafsson, Fredrik K., Martin Danelljan, Goutam Bhat, and Thomas B. Schön. "Energy-Based Models for Deep Probabilistic Regression." In Computer Vision – ECCV 2020, 325–43. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58565-5_20.

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Nkambule, Tshepo, and Ritesh Ajoodha. "Classification of Music by Genre Using Probabilistic Models and Deep Learning Models." In Proceedings of Sixth International Congress on Information and Communication Technology, 185–93. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2102-4_17.

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Hung, Alex Ling Yu, Zhiqing Sun, Wanwen Chen, and John Galeotti. "Hierarchical Probabilistic Ultrasound Image Inpainting via Variational Inference." In Deep Generative Models, and Data Augmentation, Labelling, and Imperfections, 83–92. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88210-5_7.

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Völcker, Claas, Alejandro Molina, Johannes Neumann, Dirk Westermann, and Kristian Kersting. "DeepNotebooks: Deep Probabilistic Models Construct Python Notebooks for Reporting Datasets." In Machine Learning and Knowledge Discovery in Databases, 28–43. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43823-4_3.

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Dinh, Xuan Truong, and Hai Van Pham. "Social Network Analysis Based on Combining Probabilistic Models with Graph Deep Learning." In Communication and Intelligent Systems, 975–86. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1089-9_76.

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Linghu, Yuan, Xiangxue Li, and Zhenlong Zhang. "Deep Learning vs. Traditional Probabilistic Models: Case Study on Short Inputs for Password Guessing." In Algorithms and Architectures for Parallel Processing, 468–83. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38991-8_31.

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Abid, M., Y. Ouakrim, A. Mitiche, P. A. Vendittoli, N. Hagemeister, and N. Mezghani. "A Comparative Study of End-To-End Discriminative Deep Learning Models for Knee Joint Kinematic Time Series Classification." In Biomedical Signal Processing, 33–61. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-67494-6_2.

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Liu, Zheng, and Hao Wang. "Research on Process Diagnosis of Severe Accidents Based on Deep Learning and Probabilistic Safety Analysis." In Springer Proceedings in Physics, 624–34. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1023-6_54.

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AbstractSevere accident process diagnosis provides data basis for severe accident prognosis, positive and negative effect evaluation of Severe Accident Management Guidelines (SAMGs), especially to quickly diagnose Plant Damage State (PDS) for operators in the main control room or personnel in the Technical Support Center (TSC) based on historic data of the limited number of instruments during the operation transition from Emergency Operation Procedures (EOPs) to SAMGs. This diagnosis methodology is based on tens of thousands of simulations of severe accidents using the integrated analysis program MAAP. The simulation process is organized in reference to Level 1 Probabilistic Safety Analysis (L1 PSA) and EOPs. According to L1 PSA, the initial event of accidents and scenarios from the initial event to core damage are presented in Event Trees (ET), which include operator actions following up EOPs. During simulation, the time uncertainty of operations in scenarios is considered. Besides the big data collection of simulations, a deep learning algorithm, Convolutional Neural Network (CNN), has been used in this severe accident diagnosis methodology, to diagnose the type of severe accident initiation event, the breach size, breach location, and occurrence time of the initial event of LOCA, and action time by operators following up EOPs intending to take Nuclear Power Plant (NPP) back to safety state. These algorithms train classification and regression models with ET-based numerical simulations, such as the classification model of sequence number, break location, and regression model of the break size and occurrence time of initial event MBLOCA. Then these trained models take advantage of historic data from instruments in NPP to generate a diagnosis conclusion, which is automatically written into an input deck file of MAAP. This input deck originated from previous traceback efforts and provides a numerical analysis basis for predicting the follow-up process of a severe accident, which is conducive to severe accident management. Results of this paper show a theoretical possibility that under limited available instruments, this traceback and diagnosis method can automatically and quickly diagnose PDS when operation transit from EOPs to SAMGs and provide numerical analysis basis for severe accident process prognosis.
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Rachmadi, Muhammad Febrian, Maria del C. Valdés-Hernández, Rizal Maulana, Joanna Wardlaw, Stephen Makin, and Henrik Skibbe. "Probabilistic Deep Learning with Adversarial Training and Volume Interval Estimation - Better Ways to Perform and Evaluate Predictive Models for White Matter Hyperintensities Evolution." In Predictive Intelligence in Medicine, 168–80. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87602-9_16.

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Conference papers on the topic "Deep Discriminative Probabilistic Models"

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Wang, Xin, and Siu Ming Yiu. "Classification with Rejection: Scaling Generative Classifiers with Supervised Deep Infomax." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/412.

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Deep Infomax (DIM) is an unsupervised representation learning framework by maximizing the mutual information between the inputs and the outputs of an encoder, while probabilistic constraints are imposed on the outputs. In this paper, we propose Supervised Deep InfoMax (SDIM), which introduces supervised probabilistic constraints to the encoder outputs. The supervised probabilistic constraints are equivalent to a generative classifier on high-level data representations, where class conditional log-likelihoods of samples can be evaluated. Unlike other works building generative classifiers with conditional generative models, SDIMs scale on complex datasets, and can achieve comparable performance with discriminative counterparts. With SDIM, we could perform classification with rejection. Instead of always reporting a class label, SDIM only makes predictions when test samples' largest class conditional surpass some pre-chosen thresholds, otherwise they will be deemed as out of the data distributions, and be rejected. Our experiments show that SDIM with rejection policy can effectively reject illegal inputs, including adversarial examples and out-of-distribution samples.
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Wang, Mengqiu, and Luo Si. "Discriminative probabilistic models for passage based retrieval." In the 31st annual international ACM SIGIR conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1390334.1390407.

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Lloyd, John, Yijiong Lin, and Nathan Lepora. "Probabilistic Discriminative Models address the Tactile Perceptual Aliasing Problem." In Robotics: Science and Systems 2021. Robotics: Science and Systems Foundation, 2021. http://dx.doi.org/10.15607/rss.2021.xvii.057.

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Sokolovska, Nataliya, Olivier Cappé, and François Yvon. "The asymptotics of semi-supervised learning in discriminative probabilistic models." In the 25th international conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1390156.1390280.

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Erkan, A. N., O. Kroemer, R. Detry, Y. Altun, J. Piater, and J. Peters. "Learning probabilistic discriminative models of grasp affordances under limited supervision." In 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010). IEEE, 2010. http://dx.doi.org/10.1109/iros.2010.5650088.

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van Dalen, R. C., J. Yang, H. Wang, A. Ragni, C. Zhang, and M. J. F. Gales. "Structured discriminative models using deep neural-network features." In 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU). IEEE, 2015. http://dx.doi.org/10.1109/asru.2015.7404789.

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Cetintas, Suleyman, Monica Rogati, Luo Si, and Yi Fang. "Identifying similar people in professional social networks with discriminative probabilistic models." In the 34th international ACM SIGIR conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2009916.2010123.

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Zhuowen Tu. "Probabilistic boosting-tree: learning discriminative models for classification, recognition, and clustering." In Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1. IEEE, 2005. http://dx.doi.org/10.1109/iccv.2005.194.

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Xie, Qianqian, Jimin Huang, Min Peng, Yihan Zhang, Kaifei Peng, and Hua Wang. "Discriminative Regularized Deep Generative Models for Semi-Supervised Learning." In 2019 IEEE International Conference on Data Mining (ICDM). IEEE, 2019. http://dx.doi.org/10.1109/icdm.2019.00076.

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Liu, Xixi, Che-Tsung Lin, and Christopher Zach. "Energy-based Models for Deep Probabilistic Regression." In 2022 26th International Conference on Pattern Recognition (ICPR). IEEE, 2022. http://dx.doi.org/10.1109/icpr56361.2022.9955636.

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