Results 51 to 60 of about 7,740,063 (200)

Meta-Learning Symmetries by Reparameterization

CoRR, 2020
ICLR ...
Allan Zhou, Tom Knowles, Chelsea Finn
openaire   +4 more sources

Variational Information Bottleneck Regularized Deep Reinforcement Learning for Efficient Robotic Skill Adaptation

Sensors, 2023
Deep Reinforcement Learning (DRL) algorithms have been widely studied for sequential decision-making problems, and substantial progress has been achieved, especially in autonomous robotic skill learning.
Guofei Xiang, Songyi Dian, Shaofeng Du, Zhonghui Lv   +3 more
doaj   +1 more source

Meta-learning from Learning Curves: Challenge Design and Baseline Results

, 2022
International audienceMeta-learning has been widely studied and implemented in many Automated Machine Learning systems to improve the process of selecting and training Machine Learning models for new tasks, by leveraging expertise acquired on previously ...
Grinsztajn, Nathan, Guyon, Isabelle, Sun-Hosoya, Lisheng, Nguyen, Manh Hung   +3 more
core   +1 more source

Knowledge of Accredited Social Health Activists in India: a systematic review and meta analysis of evidence drawn from primary studies published between 2005 and 2022

BMC Health Services Research
Background Since the inception of the ASHAs in the year 2005, their work horizons have increased from Reproductive, Maternal, Newborn, Child, and Adolescent health (RMNCH + A), Communicable and Non-Communicable Diseases (CD & NCD) to oral health ...
Shambhavi Singh, Bhaskar Tiwary, Manish Barik, Hemlata Arora, Sruthi Susan Abraham, Piyusha Majumdar, Daliya Sebastian, Svea Closser   +7 more
doaj   +1 more source

Availability of evidence and comparative effectiveness for surgical versus drug interventions: an overview of systematic reviews and meta-analyses

BMJ Open
Objectives This study aims to examine the prevalence of comparisons of surgery to drug regimens, the strength of evidence of such comparisons and whether surgery or the drug intervention was favoured.Design Systematic review of systematic reviews ...
Ewoud Schuit, John P Ioannidis, Emmanuel A Zavalis, Anaïs Rameau, Anirudh Saraswathula, Joachim Vist   +5 more
doaj   +1 more source

Sharing to Learn and Learning to Share; Fitting Together Meta, Multi-Task, and Transfer Learning: A Meta Review

IEEE Access
Integrating knowledge across different domains is an essential feature of human learning. Learning paradigms such as transfer learning, meta-learning, and multi-task learning reflect the human learning process by exploiting the prior knowledge for new ...
Richa Upadhyay, Ronald Phlypo, Rajkumar Saini, Marcus Liwicki   +3 more
doaj   +1 more source

A Meta-Learning Approach for Few-Shot Face Forgery Segmentation and Classification

Sensors, 2023
The technology for detecting forged images is good at detecting known forgery methods. It trains neural networks using many original and corresponding forged images created with known methods.
Yih-Kai Lin, Ting-Yu Yen
doaj   +1 more source

Towards Meta-learning over Data Streams [PDF]

, 2014
Modern society produces vast streams of data. Many stream mining algorithms have been developed to capture general trends in these streams, and make predictions for future observations, but relatively little is known about which algorithms perform ...
Pfahringer, Bernhard, van Rijn, Jan N., Holmes, Geoffrey, Vanschoren, Joaquin   +3 more
core  

Copy-Move Forgery Detection Technique Using Graph Convolutional Networks Feature Extraction

IEEE Access
In the development of image forensics, detection of Copy-Move Forgery (CMF) has become a major challenge due to the proliferation of image forgery techniques.
Varun Shinde, Vineet Dhanawat, Ahmad Almogren, Anjanava Biswas, Muhammad Bilal, Rizwan Ali Naqvi, Ateeq Ur Rehman   +6 more
doaj   +1 more source

MFPCDR: A Meta-Learning-Based Model for Federated Personalized Cross-Domain Recommendation

Applied Sciences, 2023
Cross-domain recommendation systems frequently require the use of rich source domain information to improve recommendations in the target domain, thereby resolving the data sparsity and cold-start problems, whereas the majority of existing approaches ...
Yicheng Di, Yuan Liu
doaj   +1 more source