Results 81 to 90 of about 4,969,023 (298)
Zero-Shot Deep Domain Adaptation
, 2018 Domain adaptation is an important tool to transfer knowledge about a task (e.g. classification) learned in a source domain to a second, or target domain. Current approaches assume that task-relevant target-domain data is available during training.
B Sun +7 more
core +1 more source
Findings of the Association for Computational Linguistics: ACL 2022, 2022 Findings of ACL (2022)
Chen, Junshen K. +2 more
openaire +2 more sources
Pediatric Blood &Cancer, EarlyView.ABSTRACT Introduction We developed MedSupport, a multilevel medication adherence intervention designed to address root barriers to medication adherence. This study sought to explore the feasibility and acceptability of the MedSupport intervention strategies to support a future full‐scale randomized controlled trial.
Elizabeth G. Bouchard +8 more
wiley +1 more source
Deep Unsupervised Domain Adaptation with Time Series Sensor Data: A Survey
Sensors, 2022 Sensors are devices that output signals for sensing physical phenomena and are widely used in all aspects of our social production activities. The continuous recording of physical parameters allows effective analysis of the operational status of the ...
Yongjie Shi, Xianghua Ying, Jinfa Yang
doaj +1 more source
Simultaneous Deep Transfer Across Domains and Tasks
, 2015 Recent reports suggest that a generic supervised deep CNN model trained on a large-scale dataset reduces, but does not remove, dataset bias. Fine-tuning deep models in a new domain can require a significant amount of labeled data, which for many ...
Darrell, Trevor +3 more
core +1 more source
Pathogenic Germline PALB2 and RAD50 Variants in Patients With Relapsed Ewing Sarcoma
Pediatric Blood &Cancer, EarlyView.ABSTRACT Approximately 10% of patients with Ewing sarcoma (EwS) have pathogenic germline variants. Here, we report two cases: first, a novel germline pathogenic variant in partner and localizer of BRCA2 (PALB2) in a patient with a late EwS relapse. Its impact on homologous recombination is demonstrated, and breast cancer risk is discussed.
Molly Mack +12 more
wiley +1 more source
Wasserstein Uncertainty Estimation for Adversarial Domain Matching
Frontiers in Big Data, 2022 Domain adaptation aims at reducing the domain shift between a labeled source domain and an unlabeled target domain, so that the source model can be generalized to target domains without fine tuning.
Rui Wang, Ruiyi Zhang, Ricardo Henao
doaj +1 more source
Pediatric Blood &Cancer, EarlyView.ABSTRACT Background Despite their increased risk for functional impairment resulting from cancer and its treatments, few adolescents and young adults (AYAs) with a hematological malignancy receive the recommended or therapeutic dose of exercise per week during inpatient hospitalizations.
Jennifer A. Kelleher +8 more
wiley +1 more source
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 Vanilla unsupervised domain adaptation methods tend to optimize the model with fixed neural architecture, which is not very practical in real-world scenarios since the target data is usually processed by different resource-limited devices. It is therefore of great necessity to facilitate architecture adaptation across various devices. In this paper, we
Meng, Rang +9 more
openaire +2 more sources
Pairwise Similarity for Domain Adaptation
IEEE AccessThe application of the domain adaptation technique enables the resolution of classification challenges in an unlabeled target domain by leveraging the labeled information from source domains.
Xiaoshun Wang, Sibei Luo
doaj +1 more source