Eric Gossett, 2 Cormac Toher, 2 Corey Oses, 2 Olexandr Isayev, Fleur Legrain, 5 Frisco Rose, 2 Eva Zurek, Jesús Carrete, Natalio Mingo, Alexander Tropsha, and Stefano Curtarolo 2, 8, ∗ Department of Mechanical Engineering and Materials Science, Duke University, Durham, North Carolina 27708, USA Center for Materials Genomics, Duke University, Durham, North Carolina 27708, USA Laboratory for Mole...

To provide a~istance with t~ml~ such M retrieving USENET news articles or identifying interesting Web pages, an intelligent agent requires information about a user’s interests and needs. Machine learning techniques are now being used to acquire this information. A general architecture is presented, and two approaches to learning through observation are described. An instautiation of the archite...

This paper reports on modern approaches in Information Extraction (IE) and its two main sub-tasks of Named Entity Recognition (NER) and Relation Extraction (RE). Basic concepts and the most recent approaches in this area are reviewed, which mainly include Machine Learning (ML) based approaches and the more recent trend to Deep Learning (DL)

An important goal for the machine learning (ML) community is to create approaches that can learn solutions with human-level capability. One domain where humans have held a significant advantage is visual processing. A significant approach to addressing this gap has been machine learning approaches that are inspired from the natural systems, such as artificial neural networks (ANNs), evolutionar...

Introduction Robot Learning from Demonstration (LfD) research deals with the challenges of enabling humans to teach robots novel skills and tasks (Argall et al. 2009). The practical importance of LfD is due to the fact that it is impossible to pre-program all the necessary skills and task knowledge that a robot might need during its life-cycle. This poses many interesting application areas for ...

This paper compares two methods for reen-ing uncertain knowledge bases using propo-sitional certainty-factor rules. The rst method, implemented in the Rapture system , employs neural-network training to re-ne the certainties of existing rules but uses a symbolic technique to add new rules. The second method, based on the one used in the Kbann system, initially adds a complete set of potential n...

Machine learning (ML) plays an ever-increasing role in advanced automotive functionality for driver assistance and autonomous operation; however, its adequacy from the perspective of safety certification remains controversial. In this paper, we analyze the impacts that the use of ML as an implementation approach has on ISO 26262 safety lifecycle and ask what could be done to address them. We th...

BACKGROUND Dementia is a complex disorder characterized by poor outcomes for the patients and high costs of care. After decades of research little is known about its mechanisms. Having prognostic estimates about dementia can help researchers, patients and public entities in dealing with this disorder. Thus, health data, machine learning and microsimulation techniques could be employed in develo...