Titlebook: Machine Learning and Knowledge Discovery in Databases; European Conference, Michele Berlingerio,Francesco Bonchi,Georgiana Ifr Conference p

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Toward an Understanding of Adversarial Examples in Clinical Trialsthical, when they arise. The study of adversarial examples in this area is rich in challenges for accountability and trustworthiness in ML–we highlight future directions that may be of interest to the community.

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Detecting Autism by Analyzing a Simulated Social Interactionndom-forest classifier on these features can detect autism spectrum condition accurately and functionally independently of diagnostic questionnaires. We also find that a regression model estimates the severity of the condition more accurately than the reference screening method.

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ACE-inhibitor

0302-9743 ledge Discovery in Databases, ECML PKDD 2018, held in Dublin, Ireland, in September 2018.?. The total of 131 regular papers presented in part I and part II was carefully reviewed and selected from 535 submissions; there are 52 papers in the applied data science, nectar and demo track.?.The contribut

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Image Anomaly Detection with Generative Adversarial Networkssional spaces, such as images. Inspired by recent successes in deep learning we propose a novel approach to anomaly detection using generative adversarial networks. Given a sample under consideration, our method is based on searching for a good representation of that sample in the latent space of th

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Toward an Understanding of Adversarial Examples in Clinical Trialsy studied in supervised learning, on vision tasks. However, adversarial examples in . modelling, which sits outside the traditional supervised scenario, is an overlooked challenge. We introduce the concept of ., in the context of counterfactual models for clinical trials—this turns out to introduce

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ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector a Deep Neural Network (DNN) image classifier, as demonstrated in prior work. In this work, we propose ., an attack that tackles the more challenging problem of crafting physical adversarial perturbations to fool image-based object detectors like Faster R-CNN. Attacking an object detector is more di

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