Results 31 to 40 of about 459,286 (299)

Unsupervised Domain Adaptation for Face Recognition in Unlabeled Videos

, 2017
Despite rapid advances in face recognition, there remains a clear gap between the performance of still image-based face recognition and video-based face recognition, due to the vast difference in visual quality between the domains and the difficulty of ...
Chandraker, Manmohan, Liu, Sifei, Sohn, Kihyuk, Yang, Ming-Hsuan, Yu, Xiang, Zhong, Guangyu   +5 more
core   +1 more source

Adaptive Low-Rank Methods: Problems on Sobolev Spaces [PDF]

SIAM Journal on Numerical Analysis, 2016
This paper is concerned with the development and analysis of an iterative solver for high-dimensional second-order elliptic problems based on subspace-based low-rank tensor formats. Both the subspaces giving rise to low-rank approximations and corresponding sparse approximations of lower-dimensional tensor components are determined adaptively.
Markus Bachmayr, Wolfgang Dahmen
openaire   +3 more sources

Domain Adaptation via Low Rank and Class Discriminative Representation for Autism Spectrum Disorder Identification: A Multi-Site fMRI Study

IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2023
To construct a more effective model with good generalization performance for inter-site autism spectrum disorder (ASD) diagnosis, domain adaptation based ASD diagnostic models are proposed to alleviate the inter-site heterogeneity. However, most existing
Xingdan Liu, Jiacheng Wu, Wenqi Li, Qian Liu, Lixia Tian, Huifang Huang   +5 more
doaj   +1 more source

Towards Adapting ImageNet to Reality: Scalable Domain Adaptation with Implicit Low-rank Transformations [PDF]

, 2013
Images seen during test time are often not from the same distribution as images used for learning. This problem, known as domain shift, occurs when training classifiers from object-centric internet image databases and trying to apply them directly to ...
Darrell, Trevor, Donahue, Jeff, Hoffman, Judy, Rodner, Erik, Saenko, Kate   +4 more
core  

Return of Frustratingly Easy Domain Adaptation

, 2015
Unlike human learning, machine learning often fails to handle changes between training (source) and test (target) input distributions. Such domain shifts, common in practical scenarios, severely damage the performance of conventional machine learning ...
Feng, Jiashi, Saenko, Kate, Sun, Baochen
core   +1 more source

A Quality Improvement Initiative to Standardize Pneumocystis jirovecii Pneumonia Prophylaxis in Pediatric Patients With Solid Tumors

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Background Pediatric patients with extracranial solid tumors (ST) receiving chemotherapy are at an increased risk for Pneumocystis jirovecii pneumonia (PJP). However, evidence guiding prophylaxis practices in this population is limited. A PJP‐related fatality at our institution highlighted inconsistent prescribing approaches and concerns about
Kriti Kumar, Denise M. Connolly, Ellen Kellington, Elyse Zelunka, Karla Kerrigan, Furqan Shaikh, Paul C. Nathan, Meredith S. Irwin, Sarah Cohen‐Gogo   +8 more
wiley   +1 more source

Optimizer-Aware Fine-Tuning of Whisper Small with Low-Rank Adaption: An Empirical Study of Adam and AdamW

Information
Whisper is a transformer-based multilingual model that has illustrated state-of-the-art behavior in numerous languages. However, the efficiency remains persistent with the limited computational resources.
Hadia Arshad, Tahir Abdullah, Mariam Rehman, Afzaal Hussain, Faria Kanwal, Mehwish Parveen   +5 more
doaj   +1 more source

Factor analysis modelling for speaker verification with short utterances [PDF]

, 2008
This paper examines combining both relevance MAP and subspace speaker adaptation processes to train GMM speaker models for use in speaker verification systems with a particular focus on short utterance lengths.
Lustri, Christopher, Sridharan, Sridha, Vogt, Robert   +2 more
core   +1 more source

CoLA: Collaborative Low-Rank Adaptation

Findings of the Association for Computational Linguistics: ACL 2025
The scaling law of Large Language Models (LLMs) reveals a power-law relationship, showing diminishing return on performance as model scale increases. While training LLMs from scratch is resource-intensive, fine-tuning a pre-trained model for specific tasks has become a practical alternative.
Zhou, Yiyun, Yao, Chang, Chen, Jingyuan
openaire   +2 more sources

Psychosocial Outcomes in Patients With Endocrine Tumor Syndromes: A Systematic Review

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Introduction The combination of disease manifestations, the familial burden, and varying penetrance of endocrine tumor syndromes (ETSs) is unique. This review aimed to portray and summarize available data on psychosocial outcomes in patients with ETSs and explore gaps and opportunities for future research and care.
Daniël Zwerus, Floortje Strobbe, Annemarie A. Verrijn Stuart, Hanneke M. van Santen, Gerlof D. Valk, Sasja A. Schepers, Rachel S. van Leeuwaarde   +6 more
wiley   +1 more source