Results 91 to 100 of about 685,298 (326)
Advanced Engineering Materials, EarlyView.This manuscript presents advances in digital transformation within materials science and engineering, emphasizing the role of the MaterialDigital Initiative. By testing and applying concepts such as ontologies, knowledge graphs, and integrated workflows, it promotes semantic interoperability and data‐driven innovation. The article reviews collaborative
Bernd Bayerlein +44 more
wiley +1 more source
Playing Atari with Deep Reinforcement Learning [PDF]
, 2013 We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning.
Antonoglou, Ioannis +6 more
core +1 more source
Transfer Learning in Deep Reinforcement Learning: A Survey [PDF]
arXiv, 2020 Reinforcement learning is a learning paradigm for solving sequential decision-making problems. Recent years have witnessed remarkable progress in reinforcement learning upon the fast development of deep neural networks. Along with the promising prospects of reinforcement learning in numerous domains such as robotics and game-playing, transfer learning ...
arxiv
Advanced Engineering Materials, EarlyView.The MaterialDigital initiative drives the digital transformation of material science by promoting findable, accessible, interoperable, and reusable principles and enhancing data interoperability. This article explores the role of scientific workflows, highlights challenges in their adoption, and introduces the Workflow Store as a key tool for sharing ...
Simon Bekemeier +37 more
wiley +1 more source
Application of deep reinforcement learning for Indian stock trading automation [PDF]
arXiv, 2021 In stock trading, feature extraction and trading strategy design are the two important tasks to achieve long-term benefits using machine learning techniques. Several methods have been proposed to design trading strategy by acquiring trading signals to maximize the rewards.
arxiv
Advanced Engineering Materials, EarlyView.In this study, the mechanical response of Y‐shaped core sandwich beams under compressive loading is investigated, using deep feed‐forward neural networks (DFNNs) for predictive modeling. The DFNN model accurately captures stress–strain behavior, influenced by design parameters and loading rates.
Ali Khalvandi +4 more
wiley +1 more source
Advanced Engineering Materials, EarlyView.Herein, silicon‐based nanoparticle coatings on X2CrNiMo17‐12‐2 metal powder are presented. The coating process scale, process parameters, nanoparticle size (65–200 nm) as well as the coating amount are discussed regarding powder properties. The surface roughness affects the flowability, while reflectance depends on the coating material and surface ...
Arne Lüddecke +4 more
wiley +1 more source
BMC Medical Imaging, 2022 Background Supervised deep learning in radiology suffers from notorious inherent limitations: 1) It requires large, hand-annotated data sets; (2) It is non-generalizable; and (3) It lacks explainability and intuition.
J. N. Stember, H. Shalu
doaj +1 more source
Deep Reinforcement Learning for Conversational AI [PDF]
arXiv, 2017 Deep reinforcement learning is revolutionizing the artificial intelligence field. Currently, it serves as a good starting point for constructing intelligent autonomous systems which offer a better knowledge of the visual world. It is possible to scale deep reinforcement learning with the use of deep learning and do amazing tasks such as use of pixels ...
arxiv
Review of Deep Reinforcement Learning for Autonomous Driving [PDF]
arXiv, 2023 Since deep neural networks' resurgence, reinforcement learning has gradually strengthened and surpassed humans in many conventional games. However, it is not easy to copy these accomplishments to autonomous driving because state spaces are immensely complicated in the real world and action spaces are continuous and fine control is necessary.
arxiv