Balancing misclassification errors in image-based inference using problem domain semantics and a nested cascade architecture [0.03%]
基于问题领域的语义和嵌套级联体系结构的图像基推理中的误分类平衡方法
Xin Du,Rajesh Jena,Katayoun Farrahi et al.
Xin Du et al.
Pattern recognition models, particularly neural networks, often focus on maximising classification accuracy. However, in practice, the types of errors made (misclassification between different classes) can have varying associated costs. Cur...
Deep multi-objective reinforcement learning for utility-based infrastructural maintenance optimization [0.03%]
基于效用的基础设施维护优化的深度多目标强化学习
Jesse van Remmerden,Maurice Kenter,Diederik M Roijers et al.
Jesse van Remmerden et al.
In this paper, we introduce multi-objective deep centralized multi-agent actor-critic (MO-DCMAC), a multi-objective reinforcement learning method for infrastructural maintenance optimization, an area traditionally dominated by single-object...
A fairness scale for real-time recidivism forecasts using a national database of convicted offenders [0.03%]
基于全国罪犯数据库的实时再犯罪预测公平性尺度研究
Jacob Verrey,Peter Neyroud,Lawrence Sherman et al.
Jacob Verrey et al.
This investigation explores whether machine learning can predict recidivism while addressing societal biases. To investigate this, we obtained conviction data from the UK's Police National Computer (PNC) on 346,685 records between January 1...
Gene expression clock: an unsupervised deep learning approach for predicting circadian rhythmicity from whole genome expression [0.03%]
基因表达钟：一种预测全基因组表达昼夜节律的无监督深度学习方法
Aram Ansary Ogholbake,Qiang Cheng
Aram Ansary Ogholbake
Circadian rhythms are driven by an internal molecular clock which controls physiological and behavioral processes. Disruptions in these rhythms have been associated with health issues. Therefore, studying circadian rhythms is crucial for un...
Learning in public goods games: the effects of uncertainty and communication on cooperation [0.03%]
不确定性和沟通对公共物品博弈中合作影响的实验研究
Nicole Orzan,Erman Acar,Davide Grossi et al.
Nicole Orzan et al.
Communication is a widely used mechanism to promote cooperation in multi-agent systems. In the field of emergent communication, agents are typically trained in specific environments: cooperative, competitive or mixed-motive. Motivated by th...
Anna Penzkofer,Simon Schaefer,Florian Strohm et al.
Anna Penzkofer et al.
While deep reinforcement learning (RL) agents outperform humans on an increasing number of tasks, training them requires data equivalent to decades of human gameplay. Recent hierarchical RL methods have increased sample efficiency by incorp...
Fourier convolutional decoder: reconstructing solar flare images via deep learning [0.03%]
傅立叶卷积解码器——通过深度学习重建太阳耀斑图像
Merve Selcuk-Simsek,Paolo Massa,Hualin Xiao et al.
Merve Selcuk-Simsek et al.
Reconstructing images from observational data is a complex and time-consuming process, particularly in astronomy, where traditional algorithms like CLEAN require extensive computational resources and expert interpretation to distinguish gen...
Influence-aware memory architectures for deep reinforcement learning in POMDPs [0.03%]
基于影响感知的内存架构在POMDP中的深度强化学习研究
Miguel Suau,Jinke He,Elena Congeduti et al.
Miguel Suau et al.
Due to its perceptual limitations, an agent may have too little information about the environment to act optimally. In such cases, it is important to keep track of the action-observation history to uncover hidden state information. Recent d...
Jacopo Castellini,Sam Devlin,Frans A Oliehoek et al.
Jacopo Castellini et al.
Policy gradient methods have become one of the most popular classes of algorithms for multi-agent reinforcement learning. A key challenge, however, that is not addressed by many of these methods is multi-agent credit assignment: assessing a...
A maintenance planning framework using online and offline deep reinforcement learning [0.03%]
一种结合在线和离线深度强化学习的维护计划框架
Zaharah A Bukhsh,Hajo Molegraaf,Nils Jansen
Zaharah A Bukhsh
Cost-effective asset management is an area of interest across several industries. Specifically, this paper develops a deep reinforcement learning (DRL) solution to automatically determine an optimal rehabilitation policy for continuously de...