Probabilistic Models of the Conservation and Balance Laws in Switching Regimes
Sign Up to like & getrecommendations! Published in 2018 at "Journal of Mathematical Sciences"
DOI: 10.1007/s10958-018-3701-8
Abstract: A probabilistic representation is constructed for classical solution to the Cauchy problem for system of semilinear parabolic equations such that the second order terms with different coefficients enter in diagonal way, while the lower order… read more here.
Keywords: balance laws; probabilistic models; switching regimes; conservation balance ... See more keywords
Calibration of Deep Probabilistic Models with Decoupled Bayesian Neural Networks
Sign Up to like & getrecommendations! Published in 2020 at "Neurocomputing"
DOI: 10.1016/j.neucom.2020.04.103
Abstract: Abstract Deep Neural Networks (DNNs) have achieved state-of-the-art accuracy performance in many tasks. However, recent works have pointed out that the outputs provided by these models are not well-calibrated, seriously limiting their use in critical… read more here.
Keywords: calibration; neural networks; bayesian neural; calibration deep ... See more keywords
Probabilistic Models to Assess the Seismic Safety of Rigid Block-Like Elements and the Effectiveness of Two Safety Devices
Sign Up to like & getrecommendations! Published in 2019 at "Journal of Structural Engineering"
DOI: 10.1061/(asce)st.1943-541x.0002431
Abstract: AbstractWhen subject to earthquakes, some objects and structures, such as statues, obelisks, storage systems, and transformers, show a dynamic behavior that can be modeled considering the object/st... read more here.
Keywords: models assess; assess seismic; safety; safety rigid ... See more keywords
A nested modelling approach to infrastructure performance characterisation
Sign Up to like & getrecommendations! Published in 2018 at "International Journal of Pavement Engineering"
DOI: 10.1080/10298436.2016.1172712
Abstract: Abstract Reliable and accurate predictions of infrastructure condition can save significant amounts of money for infrastructure management agencies through better planned maintenance and rehabilitation activities. Infrastructure deterioration is a complicated, dynamic and stochastic process affected… read more here.
Keywords: infrastructure deterioration; infrastructure; probabilistic models; model ... See more keywords
GMM-IL: Image Classification Using Incrementally Learnt, Independent Probabilistic Models for Small Sample Sizes
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2023.3255795
Abstract: When deep-learning classifiers try to learn new classes through supervised learning, they exhibit catastrophic forgetting issues. In this paper we propose the Gaussian Mixture Model - Incremental Learner (GMM-IL), a novel two-stage architecture that couples… read more here.
Keywords: incrementally learnt; new classes; class; sample sizes ... See more keywords
Does Interactive Conditioning Help Users Better Understand the Structure of Probabilistic Models?
Sign Up to like & getrecommendations! Published in 2023 at "IEEE transactions on visualization and computer graphics"
DOI: 10.1109/tvcg.2022.3231967
Abstract: Despite growing interest in probabilistic modeling approaches and availability of learning tools, people are hesitant to use them. There is a need for tools to communicate probabilistic models more intuitively and help users build, validate,… read more here.
Keywords: interactive conditioning; users better; help users; better understand ... See more keywords
Approximating Probabilistic Models as Weighted Finite Automata
Sign Up to like & getrecommendations! Published in 2021 at "Computational Linguistics"
DOI: 10.1162/coli_a_00401
Abstract: Abstract Weighted finite automata (WFAs) are often used to represent probabilistic models, such as n-gram language models, because among other things, they are efficient for recognition tasks in time and space. The probabilistic source to… read more here.
Keywords: finite automata; source; probabilistic models; models weighted ... See more keywords
Inherent limitations of probabilistic models for protein-DNA binding specificity
Sign Up to like & getrecommendations! Published in 2017 at "PLoS Computational Biology"
DOI: 10.1371/journal.pcbi.1005638
Abstract: The specificities of transcription factors are most commonly represented with probabilistic models. These models provide a probability for each base occurring at each position within the binding site and the positions are assumed to contribute… read more here.
Keywords: inherent limitations; protein dna; models protein; probabilistic models ... See more keywords
Probabilistic Models for the Shear Strength of RC Deep Beams
Sign Up to like & getrecommendations! Published in 2023 at "Applied Sciences"
DOI: 10.3390/app13084853
Abstract: 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… read more here.
Keywords: models shear; shear strength; model; probabilistic models ... See more keywords
Unsupervised Probabilistic Models for Sequential Electronic Health Records
Sign Up to like & getrecommendations! Published in 2022 at "Journal of biomedical informatics"
DOI: 10.48550/arxiv.2204.07292
Abstract: We develop an unsupervised probabilistic model for heterogeneous Electronic Health Record (EHR) data. Utilizing a mixture model formulation, our approach directly models sequences of arbitrary length, such as medications and laboratory results. This allows for… read more here.
Keywords: unsupervised probabilistic; electronic health; models sequential; model ... See more keywords