Results 101 to 110 of about 685,298 (326)

Beyond Order: Perspectives on Leveraging Machine Learning for Disordered Materials

Advanced Engineering Materials, EarlyView.
This article explores how machine learning (ML) revolutionizes the study and design of disordered materials by uncovering hidden patterns, predicting properties, and optimizing multiscale structures. It highlights key advancements, including generative models, graph neural networks, and hybrid ML‐physics methods, addressing challenges like data ...
Hamidreza Yazdani Sarvestani, Surabhi Nadigotti, Erfan Fatehi, Derek Aranguren van Egmond, Behnam Ashrafi   +4 more
wiley   +1 more source

The advancements and applications of deep reinforcement learning in Go [PDF]

ITM Web of Conferences
Combining Deep Learning's perceptual skills with Reinforcement Learning's decision-making abilities, Deep Reinforcement Learning (DRL) represents a significant breakthrough in Artificial Intelligence (AI).
Zheng Xutao
doaj   +1 more source

Placement Optimization with Deep Reinforcement Learning [PDF]

arXiv, 2020
Placement Optimization is an important problem in systems and chip design, which consists of mapping the nodes of a graph onto a limited set of resources to optimize for an objective, subject to constraints. In this paper, we start by motivating reinforcement learning as a solution to the placement problem.
arxiv  

Biomimetic Design of Biocompatible Neural Probes for Deep Brain Signal Monitoring and Stimulation: Super Static Interface for Immune Response‐Enhanced Contact

Advanced Functional Materials, EarlyView.
Ultrathin, flexible neural probes are developed with an innovative, biomimetic design incorporating brain tissue‐compatible materials. The material system employs biomolecule‐based encapsulation agents to mitigate inflammatory responses, as demonstrated through comprehensive in vitro and in vivo studies.
Jeonghwa Jeong, Gyeonghwa Heo, Young Woo Kwon, Seon Yeong Chae, Min Jae Kim, Ki Jun Yu, Hwa Kyoung Shin, Suck Won Hong   +7 more
wiley   +1 more source

Deep reinforcement learning to detect brain lesions on MRI: a proof-of-concept application of reinforcement learning to medical images [PDF]

arXiv, 2020
Purpose: AI in radiology is hindered chiefly by: 1) Requiring large annotated data sets. 2) Non-generalizability that limits deployment to new scanners / institutions. And 3) Inadequate explainability and interpretability. We believe that reinforcement learning can address all three shortcomings, with robust and intuitive algorithms trainable on small ...
arxiv  

Deep Reinforcement Learning Boosted by External Knowledge [PDF]

, 2017
Recent improvements in deep reinforcement learning have allowed to solve problems in many 2D domains such as Atari games. However, in complex 3D environments, numerous learning episodes are required which may be too time consuming or even impossible especially in real-world scenarios. We present a new architecture to combine external knowledge and deep
arxiv   +1 more source

Chiral Engineered Biomaterials: New Frontiers in Cellular Fate Regulation for Regenerative Medicine

Advanced Functional Materials, EarlyView.
Chiral engineered biomaterials can selectively influence cell behaviors in regenerative medicine. This review covers chiral engineered biomaterials in terms of their fabrication methods, cellular response mechanisms, and applications in directing stem cell differentiation and tissue function.
Yuwen Wang, Xin Zhang, Denghui Xie, Chunying Chen, Zhifeng Huang, Zhong Alan Li   +5 more
wiley   +1 more source

Tracking the Race Between Deep Reinforcement Learning and Imitation Learning -- Extended Version [PDF]

arXiv, 2020
Learning-based approaches for solving large sequential decision making problems have become popular in recent years. The resulting agents perform differently and their characteristics depend on those of the underlying learning approach. Here, we consider a benchmark planning problem from the reinforcement learning domain, the Racetrack, to investigate ...
arxiv  

Stratum Corneum‐Inspired Zwitterionic Hydrogels with Intrinsic Water Retention and Anti‐Freezing Properties for Intelligent Flexible Sensors

Advanced Functional Materials, EarlyView.
A novel stratum corneum‐inspired zwitterionic hydrogel is developed for intelligent, flexible sensors, featuring intrinsic water retention and anti‐freezing properties. The quasi‐gel, composed of hygroscopic polymers and bound water, maintains its softness across a wide range of humidity.
Meng Wu, Chenyu Qiao, Peng‐Fei Sui, Jing‐Li Luo, Zuoli Li, Yi Cao, Renjun Pei, Xuwen Peng, Hongbo Zeng   +8 more
wiley   +1 more source

A Deep Hierarchical Reinforcement Learning Algorithm in Partially Observable Markov Decision Processes

IEEE Access, 2018
In recent years, reinforcement learning (RL) has achieved remarkable success due to the growing adoption of deep learning techniques and the rapid growth of computing power.
Tuyen P. Le, Ngo Anh Vien, TaeChoong Chung   +2 more
doaj   +1 more source