text: 11,254,1403330996041363459,1159548392839753729,Hugo Yeche,"Can contrastive learning help in applications where similar samples don't share downstream task labels?  In our #ICML2021 paper, we propose a simple framework for such problems and apply it to the monitoring of patients in the ICU  Paper: <LINK> <LINK> Inspired by the similarity existing between contiguous states of a patient, we introduce a new contrastive objective. It allows a trade-off between preserving or discarding predefined attributes and this independently of data-augmentation. <LINK> We call samples sharing these chosen attributes “neighbors” and define it as a binary function. It allows unifying prior works, unsupervised and supervised, under a common framework. <LINK> This is my first work as a Ph.D. student at @CSatETH and I’m very grateful to @gideoknite, our collaborators @FrancescoLocat8 and Matthias Hüser, and my supervisor @gxr for their help and guidance throughout this process!",https://arxiv.org/abs/2106.05142,"Intensive care units (ICU) are increasingly looking towards machine learning for methods to provide online monitoring of critically ill patients. In machine learning, online monitoring is often formulated as a supervised learning problem. Recently, contrastive learning approaches have demonstrated promising improvements over competitive supervised benchmarks. These methods rely on well-understood data augmentation techniques developed for image data which do not apply to online monitoring. In this work, we overcome this limitation by supplementing time-series data augmentation techniques with a novel contrastive learning objective which we call neighborhood contrastive learning (NCL). Our objective explicitly groups together contiguous time segments from each patient while maintaining state-specific information. Our experiments demonstrate a marked improvement over existing work applying contrastive methods to medical time-series. ",Neighborhood Contrastive Learning Applied to Online Patient Monitoring,4,"[""Can contrastive learning help in applications where similar samples don't share downstream task labels? \n\nIn our #ICML2021 paper, we propose a simple framework for such problems and apply it to the monitoring of patients in the ICU \n\nPaper: <LINK> <LINK>"", 'Inspired by the similarity existing between contiguous states of a patient, we introduce a new contrastive objective. It allows a trade-off between preserving or discarding predefined attributes and this independently of data-augmentation. https://t.co/92hlkptBGM', 'We call samples sharing these chosen attributes “neighbors” and define it as a binary function. It allows unifying prior works, unsupervised and supervised, under a common framework. https://t.co/supfRvc8ZR', 'This is my first work as a Ph.D. student at @CSatETH  and I’m very grateful to @gideoknite, our collaborators @FrancescoLocat8 and Matthias Hüser, and my supervisor @gxr  for their help and guidance throughout this process!']",21,06,911