To this end, in this paper, we consider the problem of online learning in linear stochastic contextual bandit problems with endogenous covariates.
Alzheimer's disease (AD) is a heterogeneous, multifactorial neurodegenerative disorder characterized by beta-amyloid, pathologic tau, and neurodegeneration.
Dialogue systems capable of social influence such as persuasion, negotiation, and therapy, are essential for extending the use of technology to numerous realistic scenarios.
Complex conversation settings such as persuasion involve communicating changes in attitude or behavior, so users' perspectives need to be addressed, even when not directly related to the topic.
To obtain a high-quality model, an incentive mechanism is necessary to motivate more high-quality workers with data and computing power.
Despite the striking efforts in investigating neurobiological factors behind the acquisition of beta-amyloid (A), protein tau (T), and neurodegeneration ([N]) biomarkers, the mechanistic pathways of how AT[N] biomarkers spread throughout the brain remain elusive.
Federated learning trains models across devices with distributed data, while protecting the privacy and obtaining a model similar to that of centralized ML.
The dataset and classifiers contribute to monitoring and tracking of vaccine discussions for social scientific and public health efforts in combating the problem of vaccine misinformation.
To develop intervention chatbots, the first step is to understand natural language conversation strategies in human conversation.
Using chatbots to deliver recommendations is increasingly popular.
Three major biomarkers: beta-amyloid (A), pathologic tau (T), and neurodegeneration (N), are recognized as valid proxies for neuropathologic changes of Alzheimer's disease.
Currently, many studies of Alzheimer's disease (AD) are investigating the neurobiological factors behind the acquisition of beta-amyloid (A), pathologic tau (T), and neurodegeneration ([N]) biomarkers from neuroimages.
Given a 3D object in isolation, our functional similarity network (fSIM-NET), a variation of the triplet network, is trained to predict the functionality of the object by inferring functionality-revealing interaction contexts.
Developing intelligent persuasive conversational agents to change people's opinions and actions for social good is the frontier in advancing the ethical development of automated dialogue systems.