Results 41 to 50 of about 389,599 (264)
Interventional Domain Adaptation
CoRR, 2020 Domain adaptation (DA) aims to transfer discriminative features learned from source domain to target domain. Most of DA methods focus on enhancing feature transferability through domain-invariance learning. However, source-learned discriminability itself might be tailored to be biased and unsafely transferable by spurious correlations, \emph{i.e ...
Jun Wen 0001 +6 more
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Domain Conditional Predictors for Domain Adaptation
CoRR, 2021 Learning guarantees often rely on assumptions of i.i.d. data, which will likely be violated in practice once predictors are deployed to perform real-world tasks. Domain adaptation approaches thus appeared as a useful framework yielding extra flexibility in that distinct train and test data distributions are supported, provided that other assumptions ...
João Monteiro +4 more
openaire +3 more sources
Meta Domain Adaptation Approach for Multi-Domain Ranking
IEEE AccessIn a real industry recommendation system, the distribution of recommended domains is very redundant. Different domains may address the same problem, such as the Click-Through Rate (CTR) prediction, and may share the same features.
Zihan Xia +4 more
doaj +1 more source
CoRR, 2020 Standard supervised machine learning assumes that the distribution of the source samples used to train an algorithm is the same as the one of the target samples on which it is supposed to make predictions. However, as any data scientist will confirm, this is hardly ever the case in practice.
Pirmin Lemberger, Ivan Panico
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Pediatric Blood &Cancer, EarlyView.ABSTRACT Background Shwachman–Diamond syndrome (SDS) is a rare autosomal recessive ribosomopathy characterized by bone marrow failure and multisystem involvement, with emerging evidence of associated neurocognitive impairment. Methods We conducted a retrospective study of 240 individuals with biallelic Shwachman–Bodian–Diamond syndrome (SBDS) mutations
Jane Koo +11 more
wiley +1 more source
Self Domain Adapted Network [PDF]
, 2020 Domain shift is a major problem for deploying deep networks in clinical practice. Network performance drops significantly with (target) images obtained differently than its (source) training data. Due to a lack of target label data, most work has focused on unsupervised domain adaptation (UDA).
Yufan He +4 more
openaire +3 more sources
Pediatric Blood &Cancer, EarlyView.ABSTRACT Purpose Next‐generation sequencing (NGS) has emerged as a promising approach to improve diagnostic accuracy, but its feasibility in low‐ and middle‐income countries remains unknown. This study characterized the diagnostic landscape and assessed organizational readiness for NGS implementation at two childhood cancer treatment centers in Accra ...
Melissa Carvalho +6 more
wiley +1 more source
Self-Supervised Domain Adaptation for Computer Vision Tasks
IEEE Access, 2019 Recent progress of self-supervised visual representation learning has achieved remarkable success on many challenging computer vision benchmarks. However, whether these techniques can be used for domain adaptation has not been explored.
Jiaolong Xu +2 more
doaj +1 more source
Pediatric Blood &Cancer, EarlyView.ABSTRACT Background Survivors of pediatric brain tumors (PBTs) can experience long‐term social difficulties, impacting quality of life. Beyond medical and environmental factors, family psychosocial risk may play a role in social outcomes by shaping the caregiving environment and may provide intervention options.
Renske H. Houben +4 more
wiley +1 more source
Therapeutic Apheresis and Dialysis, EarlyView.ABSTRACT Background Patients with chronic kidney disease undergoing hemodialysis commonly experience reduced physical function, fatigue, poor sleep quality, and impaired health‐related quality of life. Intradialytic exercise has been proposed as a non‐pharmacological strategy to improve these outcomes.
Klebson da Silva Almeida +6 more
wiley +1 more source