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Enhancing deep learning models for campaign participation prediction
(2019-07-31)
Companies engage with their customers in order to establish a long-term relationship. Targeting the right audience with the right product is crucial for providing better services to customers, increasing their loyalty to ...
DNN-based speaker-adaptive postfiltering with limited adaptation data for statistical speech synthesis systems
(IEEE, 2019)
Deep neural networks (DNNs) have been successfully deployed for acoustic modelling in statistical parametric speech synthesis (SPSS) systems. Moreover, DNN-based postfilters (PF) have also been shown to outperform conventional ...
Image denoising using deep convolutional autoencoders
(2019-08-19)
Image denoising is one of the fundamental problems in image processing eld since it is required by many computer vision applications. Various approaches have been used in image denoising throughout the years from spatial ...
Description-aware fashion image inpainting with convolutional neural networks in coarse-to-fine manner
(The ACM Digital Library, 2020-04-14)
Inpainting a particular missing region in an image is a challenging vision task, and promising improvements on this task have been achieved with the help of the recent developments in vision-related deep learning studies. ...
Not all mistakes are equal
(The ACM Digital Library, 2020)
In many tasks, classifiers play a fundamental role in the way an agent behaves. Most rational agents collect sensor data from the environment, classify it, and act based on that classification. Recently, deep neural networks ...
An actor-critic reinforcement learning approach for bilateral negotiation
Designing an effective and intelligent bidding strategy is one of the most compelling research challenges in automated negotiation, where software agents negotiate with each other to find a mutual agreement when there is ...
Campaign participation prediction with deep learning
(Elsevier, 2021-08)
Increasingly, on-demand nature of customer interactions put pressure on companies to build real-time campaign management systems. Instead of having managers to decide on the campaign rules, such as, when, how and whom to ...
Learning system dynamics via deep recurrent and conditional neural systems
(IEEE, 2021)
Although there are various mathematical methods for modeling system dynamics, more general solutions can be achieved using deep learning based on data. Alternative deep learning methods are presented in parallel with the ...
Neural network estimatorsfor optimal tour lengths of TSP instances with arbitrary node distributions
To achieve operational efficiency in logistics, we need to solve complex routing problems. Due to their complexity, these problems are often solved sequentially, i.e., using cluster-first route-second (CFRS) type frameworks. ...
Graph convolutional network-based deep feature learning for cardiovascular disease recognition from heart sound signals
(Wiley, 2022-12)
The high mortality rate and prevalence of cardiovascular disease (CVD) make early detection of the disease essential. Due to its simplicity and low cost, the phonocardiogram (PCG) system is widely used in healthcare ...
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