Results 51 to 60 of about 29,104 (263)

Active Instance Selection for Few-Shot Classification

IEEE Access, 2022
Few-shot learning aims to develop well-trained models by using only a few annotated samples. However, the performance of few-shot learning deteriorates if inappropriate support samples are selected.
Junsup Shin, Youngwook Kang, Seungjin Jung, Jongwon Choi   +3 more
doaj   +1 more source

Accurate and noninvasive prostate cancer detection using plasma‐derived extracellular vesicle RNA

FEBS Open Bio, EarlyView.
Plasma extracellular vesicles were captured with WGA‐conjugated magnetic beads and profiled for RNA biomarkers. A three‐RNA panel (NM_024955, NR_047469, and NR_002564) distinguished prostate cancer from healthy controls and benign prostatic hyperplasia, supporting a simple, noninvasive approach to improve prostate cancer detection.
Hanping Wei, Haoran Wu, Wei Feng
wiley   +1 more source

Long‐Term Follow‐Up of Chemotherapy‐Associated Biological Aging in Women With Early Breast Cancer

Aging and Cancer, EarlyView.
Women threated with adjuvant chemotherapy for early breast cancer have sustained long‐term increase in p16INK4a,, a robust marker of cell senescence, suggesting a chemotherapy‐associated age acceleration. p16INK4a as well as other biomarkers may identify patients at greatest risk for senescence‐related diseases of aging.
Hyman B. Muss, Alexis C. Wardell, Allison M. Deal, Kirsten A. Nyrop, Allison Ross, Yara G. Abdou, Jeffrey D. Aldrich, Lisa A. Cary, E. Claire Dees, Katherine E. Reeder‐Hayes, Emily M. Ray, Natalia Mitin, Denis Tsygankov   +12 more
wiley   +1 more source

Hybrid attentive prototypical network for few-shot action recognition

Complex & Intelligent Systems
Most previous few-shot action recognition works tend to process video temporal and spatial features separately, resulting in insufficient extraction of comprehensive features.
Zanxi Ruan, Yingmei Wei, Yanming Guo, Yuxiang Xie   +3 more
doaj   +1 more source

A New Instrument Monitoring Method Based on Few-Shot Learning

Applied Sciences, 2023
As an important part of the industrialization process, fully automated instrument monitoring and identification are experiencing an increasingly wide range of applications in industrial production, autonomous driving, and medical experimentation. However,
Beini Zhang, Liping Li, Yetao Lyu, Shuguang Chen, Lin Xu, Guanhua Chen   +5 more
doaj   +1 more source

Self‐Regulated Learning Meets AI: Reinterpreting Self‐Regulation, Co‐Regulation, and Socially Shared Regulation in Human–AI Interaction

New Directions for Adult and Continuing Education, EarlyView.
ABSTRACT Advancing artificial intelligence (AI) has transformed learning and work, yet higher education and professional development programs have not systematically equipped learners for AI‐prevalent environments. This lack of preparation creates uncertainty regarding control, responsibility, trust, and accountability.
Moon‐Heum Cho, Jerusalem Merkebu
wiley   +1 more source

From Few-Shot Optimal Control to Few-Shot Learning

CoRR
6 ...
Roman A. Chertovskih, Nikolay Pogodaev, Maxim V. Staritsyn, A. Pedro Aguiar   +3 more
openaire   +2 more sources

Meta-Transfer Learning for Few-Shot Learning

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019
CVPR ...
Qianru Sun, Yaoyao Liu 0001, Tat-Seng Chua, Bernt Schiele   +3 more
openaire   +5 more sources

Few-shot Learning for Spatial Regression

CoRR, 2020
We propose a few-shot learning method for spatial regression. Although Gaussian processes (GPs) have been successfully used for spatial regression, they require many observations in the target task to achieve a high predictive performance. Our model is trained using spatial datasets on various attributes in various regions, and predicts values on ...
Tomoharu Iwata, Yusuke Tanaka 0002
openaire   +2 more sources

Prototype Completion for Few-Shot Learning

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023
Few-shot learning aims to recognize novel classes with few examples. Pre-training based methods effectively tackle the problem by pre-training a feature extractor and then fine-tuning it through the nearest centroid based meta-learning. However, results show that the fine-tuning step makes marginal improvements.
Baoquan Zhang, Xutao Li 0003, Yunming Ye, Shanshan Feng 0001   +3 more
openaire   +3 more sources