Results 71 to 80 of about 476,790 (313)

Learning Invariant Representation for Continual Learning

CoRR, 2021
Accepted at the AAAI Meta-Learning for Computer Vision Workshop (2021)
Sokar, Ghada A.Z.N., Mocanu, Decebal Constantin, Pechenizkiy, Mykola   +2 more
openaire   +3 more sources

Research and development of network representation learning

网络与信息安全学报, 2019
Network representation learning is a bridge between network raw data and network application tasks which aims to map nodes in the network to vectors in the low-dimensional space.These vectors can be used as input to the machine learning model for social ...
Ying YIN, Lixin JI, Ruiyang HUANG, Lixin DU   +3 more
doaj   +3 more sources

Liquid biopsy‐based diagnostic evaluation of hypermethylated CpG sites for ovarian cancer diagnosis

Molecular Oncology, EarlyView.
This schematic outlines the workflow from biomarker identification to duplex MethyLight assay validation for epithelial ovarian cancer diagnosis using cfDNA‐based liquid biopsy. Initial screening of hypermethylated CpG candidates (cg02957270, cg10061138 cg00480298, COL2A1) was performed in tissue using ARMS‐PCR, COBRA, qPCR and image analysis. Selected
Deepa Bisht, Sameer Gupta, Amrita Chaurasia, Manisha Sachan   +3 more
wiley   +1 more source

Disentangled Representation Learning

IEEE Transactions on Pattern Analysis and Machine Intelligence
Accepted by IEEE Transactions on Pattern Analysis and Machine ...
Xin Wang 0019, Hong Chen 0011, Si'ao Tang, Zihao Wu 0005, Wenwu Zhu 0001   +4 more
openaire   +3 more sources

Global representation fine-tuning for federated self-supervised representation learning

International Journal of Intelligent Networks
Federated self-supervised representation learning combines federated learning with self-supervised mechanisms to learn general representations from distributed unlabeled data, effectively reducing reliance on labeled data.
Hongzi Li, Guifen Zhang, Qinchun Su, Lina Ge   +3 more
doaj   +1 more source

Network Representation Based on the Joint Learning of Three Feature Views

Big Data Mining and Analytics, 2019
Network representation learning plays an important role in the field of network data mining. By embedding network structures and other features into the representation vector space of low dimensions, network representation learning algorithms can provide
Zhonglin Ye, Haixing Zhao, Ke Zhang, Zhaoyang Wang, Yu Zhu   +4 more
doaj   +1 more source

Representation Discovery for Kernel-Based Reinforcement Learning [PDF]

, 2015
Recent years have seen increased interest in non-parametric reinforcement learning. There are now practical kernel-based algorithms for approximating value functions; however, kernel regression requires that the underlying function being approximated be ...
Zewdie, Dawit H., Konidaris, George
core  

Patient therapy outcome modeling in cancer organoids is improved by cancer‐associated fibroblasts and organoid assembly convolution

Molecular Oncology, EarlyView.
Patient‐derived organoids (PDOs) from pancreatic, colorectal, and gastric cancers were used to evaluate standard and experimental therapies. Incorporating cancer‐associated fibroblasts (CAFs) into organoid cultures improved patient therapy outcome prediction.
Marcin Grochowski, Liudmyla Dolinchuk, Michał Jerzak, Albert Gandurski, Tomasz Grochowski, Weronika Wojtyś, Maciej Zadrożny, Wojciech Kaźmierczak, Małgorzata Lenarcik, Marta Matejak‐Górska, Radosław Samsel, Tomasz Olesiński, Dawid Walerych   +12 more
wiley   +1 more source

Learning A Disentangling Representation For PU Learning

CoRR, 2023
In this paper, we address the problem of learning a binary (positive vs. negative) classifier given Positive and Unlabeled data commonly referred to as PU learning. Although rudimentary techniques like clustering, out-of-distribution detection, or positive density estimation can be used to solve the problem in low-dimensional settings, their efficacy ...
Omar Zamzam, Haleh Akrami, Mahdi Soltanolkotabi, Richard M. Leahy   +3 more
openaire   +2 more sources

Epigenetic heterogeneity and plasticity in therapy‐induced tumor states through single‐cell multi‐omics

Molecular Oncology, EarlyView.
Single‐cell multi‐omics reveals epigenetic heterogeneity across therapy‐adaptive tumor states, including quiescent/dormant, drug‐tolerant persister, and EMT‐like phenotypes. By linking regulatory features with state‐associated biomarkers, these approaches inform biomarker‐guided therapeutic strategies for evolving tumors.
Hee Jung Kim, Hwiyeong Lee, Jin Hong, Daechan Park   +3 more
wiley   +1 more source