Results 11 to 20 of about 60,104 (158)
Explainable deep learning for healthcare workforce attrition: a methodological study on the Watson healthcare synthetic benchmark [PDF]
Frontiers in Public HealthBackgroundRetaining qualified nurses and allied-health staff has become a central occupational-health concern for modern hospital systems, yet existing attrition-prediction models face a persistent trade-off between predictive accuracy and auditability ...
Dan Yin, Xinyu Lu, Tingyi Mei
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Learning Synthetic Environments and Reward Networks for Reinforcement Learning
CoRR, 2022 We introduce Synthetic Environments (SEs) and Reward Networks (RNs), represented by neural networks, as proxy environment models for training Reinforcement Learning (RL) agents. We show that an agent, after being trained exclusively on the SE, is able to solve the corresponding real environment. While an SE acts as a full proxy to a real environment by
Fabio Ferreira +3 more
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Denoising-Based Decoupling-Contrastive Learning for Ubiquitous Synthetic Face Images
IEEE Access, 2023 With the improvement of generative models such as GPT-4, GANs, and diffusion models, synthetic face images are increasingly pervading the current digital environment.
Yupeng Zhu, Xinyi Shen, Peilun Du
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Education Sciences, 2021 This document evidences an innovative methodological vision in the teaching-learning process of the English language focused on the inclusion of all its students in a heterogeneous learning environment. Learning a foreign language as a second language is
Jorge Cárdenas, Esteban Inga
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Applied Sciences, 2022 This paper aims at analyzing the performance of reinforcement learning (RL) agents when trained in environments created by a generative adversarial network (GAN).
Smita Mahajan +7 more
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Boosting Deep Reinforcement Learning Agents with Generative Data Augmentation
Applied Sciences, 2023 Data augmentation is a promising technique in improving exploration and convergence speed in deep reinforcement learning methodologies. In this work, we propose a data augmentation framework based on generative models for creating completely novel states
Tasos Papagiannis +2 more
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Applied Sciences, 2022 This paper presents a novel approach to training a real-world object detection system based on synthetic data utilizing state-of-the-art technologies. Training an object detection system can be challenging and time-consuming as machine learning requires ...
Ingeborg Rasmussen +4 more
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Reinforcement Learning in an Environment Synthetically Augmented with Digital Pheromones [PDF]
Advances in Artificial Intelligence, 2014 Reinforcement learning requires information about states, actions, and outcomes as the basis for learning. For many applications, it can be difficult to construct a representative model of the environment, either due to lack of required information or because of that the model's state space may become too large to allow a solution in a reasonable ...
Salvador E. Barbosa, Mikel D. Petty
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Asynchronous Deep Double Dueling Q-learning for trading-signal execution in limit order book markets
Frontiers in Artificial Intelligence, 2023 We employ deep reinforcement learning (RL) to train an agent to successfully translate a high-frequency trading signal into a trading strategy that places individual limit orders.
Peer Nagy +4 more
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Denoising Diffusion Probabilistic Models and Transfer Learning for citrus disease diagnosis
Frontiers in Plant Science, 2023 ProblemsPlant Disease diagnosis based on deep learning mechanisms has been extensively studied and applied. However, the complex and dynamic agricultural growth environment results in significant variations in the distribution of state samples, and the ...
Yuchen Li +4 more
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