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Now showing items 1069-1088 of 5783
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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 Q-learning based optimization of VLC systems with dynamic time-division multiplexing
(IEEE, 2020)The traditional method to solve nondeterministic-polynomial-time (NP)-hard optimization problems is to apply meta-heuristic algorithms. In contrast, Deep Q Learning (DQL) uses memory of experience and deep neural network ... -
Deep reinforcement based power allocation for the max-min optimization in non-orthogonal multiple access
(IEEE, 2020)NOMA is a radio access technique that multiplexes several users over the frequency resource and provides high throughput and fairness among different users. The maximization of the minimum the data-rate, also known as ... -
Deep reinforcement learning approach for trading automation in the stock market
Deep Reinforcement Learning (DRL) algorithms can scale to previously intractable problems. The automation of profit generation in the stock market is possible using DRL, by combining the financial assets price "prediction" ... -
Deep reinforcement learning approach for trading automation in the stock market
(IEEE, 2022)Deep Reinforcement Learning (DRL) algorithms can scale to previously intractable problems. The automation of profit generation in the stock market is possible using DRL, by combining the financial assets price 'prediction' ... -
Deep reinforcement learning for acceptance strategy in bilateral negotiations
(TÜBİTAK, 2020)This paper introduces an acceptance strategy based on reinforcement learning for automated bilateral negotiation, where negotiating agents bargain on multiple issues in a variety of negotiation scenarios. Several acceptance ... -
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 ... -
Deepsym: Deep symbol generation and rule learning for planning from unsupervised robot interaction
(AI Access Foundation, 2022)Symbolic planning and reasoning are powerful tools for robots tackling complex tasks. However, the need to manually design the symbols restrict their applicability, especially for robots that are expected to act in open-ended ... -
Defeating populists: The case of 2019 Istanbul elections
(Taylor & Francis, 2021)How can populist competitive authoritarian regimes be defeated through elections? In this article, we focus on the 2019 municipal campaign strategy of the opposition Istanbul candidate Ekrem İmamoglu as a case study of a ... -
Defect-aware nanocrossbar logic mapping through matrix canonization using two-dimensional radix sort
(ACM, 2011-08)Nanocrossbars (i.e., nanowire crossbars) offer extreme logic densities but come with very high defect rates; stuck-open/closed, broken nanowires. Achieving reasonable yield and utilization requires logic mapping that is ... -
Define
(1925-04) -
Defining architectural viewpoints for quality concerns
(Springer Science+Business Media, 2011)A common practice in software architecture design is to apply architectural views to model the design decisions for the various stakeholder concerns. When dealing with quality concerns, however, it is more difficult to ... -
Dehşetler içinde
(1910-06) -
Delegation vs. control of component procurement under asymmetric cost information and simple contracts
(Informs, 2013)A manufacturer must choose whether to delegate component procurement to her tier 1 supplier or control it directly. Because of information asymmetry about suppliers’ production costs and the use of simple quantity discount ... -
Deli
(1922-03) -
Deli
(1888-06) -
Delivery phase in cache-based wireless networks with modified LT codes
(Springer Nature, 2020-10)Caching has emerged as an efficient technique to reduce delivery latency and network congestion. The focus of this paper is on content delivery in the caching-based wireless systems. In view of the fact that in such systems, ... -
Demand and supply planning for Covid-19 vaccine in Turkey with an anti-vaccine survey
The Covid-19 virus, which affects many people, continues in a very contagious state. Vaccination is necessary to end this pandemic period, which everyone wants to end as soon as possible. The studies in the literature ...
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