Browsing by Subject "Deep learning"
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ACNMP: skill transfer and task extrapolation through learning from demonstration and reinforcement learning via representation sharing
(ML Research Press, 2020)To equip robots with dexterous skills, an effective approach is to first transfer the desired skill via Learning from Demonstration (LfD), then let the robot improve it by self-exploration via Reinforcement Learning (RL). ... -
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 ... -
Applying deep learning models to twitter data to detect airport service quality
(Elsevier, 2021-03)Measuring airport service quality (ASQ) is an important process for identifying shortages and suggesting improvements that guide management decisions. This research, introduces a general framework for measuring ASQ using ... -
Asset price and direction prediction via deep 2D transformer and convolutional neural networks
(ACM, 2022-11-02)Artificial intelligence-based algorithmic trading has recently started to attract more attention. Among the techniques, deep learning-based methods such as transformers, convolutional neural networks, and patch embedding ... -
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 ... -
CheckMate: English grammatical error correction for native Turkish speakers
(IEEE, 2023)The covid-19 outbreak left many countries have no choice but turn to online education. Turkish students, who were among those who were affected, faced difficulties in improving their English as the opportunity to have ... -
CL-FedFR: Curriculum learning for federated face recognition
(SciTePress, 2024-02-29)Face recognition (FR) has been significantly enhanced by the advent and continuous improvement of deep learning algorithms and accessibility of large datasets. However, privacy concerns raised by using and distributing face ... -
Curriculum learning for face recognition
(IEEE, 2021)We present a novel curriculum learning (CL) algorithm for face recognition using convolutional neural networks. Curriculum learning is inspired by the fact that humans learn better, when the presented information is organized ... -
Deep learning based event recognition in aerial imagery
(IEEE, 2023)In this paper, we investigate event recognition for aerial surveillance. This is a significant task especially when we consider the growing popularity of UAVs. The main purpose of the paper is to detect events both at the ... -
Deep learning-based expressive speech synthesis: a systematic review of approaches, challenges, and resources
(Springer, 2024-02-12)Speech synthesis has made significant strides thanks to the transition from machine learning to deep learning models. Contemporary text-to-speech (TTS) models possess the capability to generate speech of exceptionally high ... -
Deep learning-based speaker-adaptive postfiltering with limited adaptation data for embedded text-to-speech synthesis systems
(Elsevier, 2023-06)End-to-end (e2e) speech synthesis systems have become popular with the recent introduction of text-to-spectrogram conversion systems, such as Tacotron, that use encoder–decoder-based neural architectures. Even though those ... -
Deep multi-object symbol learning with self-attention based predictors
(IEEE, 2023)This paper proposes an architecture that can learn symbolic representations from the continuous sensorimotor experience of a robot interacting with a varying number of objects. Unlike previous works, this work aims to ... -
Deep transformer-based asset price and direction prediction
(IEEE, 2024)The field of algorithmic trading, driven by deep learning methodologies, has garnered substantial attention in recent times. Within this domain, transformers, convolutional neural networks, and patch embedding-based ... -
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. ... -
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 ... -
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 ... -
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 ... -
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 ... -
Lane type classification & distance measurement system for autonomous vehicle
(IEEE, 2023)In this paper, lane type classification and lane distance measurement system are proposed for autonomous vehicles. In the proposed system, the perspective transformation method is applied to the image taken from the vehicle ... -
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 ...
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