Bayesian Semi-supervised Learning with Graph Gaussian Processes [PDF]
, 2018 We propose a data-efficient Gaussian process-based Bayesian approach to the semi-supervised learning problem on graphs. The proposed model shows extremely competitive performance when compared to the state-of-the-art graph neural networks on semi ...
Colombo, Nicolo +2 more
core +1 more source
Néel Tensor Torque in Polycrystalline Antiferromagnets
Advanced Materials, EarlyView.This work introduces a Néel tensor torque based on a rank‐two symmetric tensor capturing spin correlations in a polycrystalline antiferromagnet. It shows the Néel tensor can be shaped and reshaped through the spin‐orbit torque (SOT) technique, enabling field‐free SOT switching with a specific polarity of the adjacent ferromagnet. This discovery opens a
Chao‐Yao Yang +4 more
wiley +1 more source
Semi-supervised Classification Based Mixed Sampling for Imbalanced Data
Open Physics, 2019 In practical application, there are a large amount of imbalanced data containing only a small number of labeled data. In order to improve the classification performance of this kind of problem, this paper proposes a semi-supervised learning algorithm ...
Zhao Jianhua, Liu Ning
doaj +1 more source
Materials and System Design for Self‐Decision Bioelectronic Systems
Advanced Materials, EarlyView.This review highlights how self‐decision bioelectronic systems integrate sensing, computation, and therapy into autonomous, closed‐loop platforms that continuously monitor and treat diseases, marking a major step toward intelligent, self‐regulating healthcare technologies.
Qiankun Zeng +9 more
wiley +1 more source
Graph regularized low-rank representation for semi-supervised learning
Journal of Algorithms & Computational Technology, 2021 Low-rank representation (LRR) has attracted wide attention of researchers in recent years due to its excellent performance in the exploration of high-dimensional subspace structures.
Cong-Zhe You +3 more
doaj +1 more source
Asymptotic Analysis of Generative Semi-Supervised Learning [PDF]
, 2010 Semisupervised learning has emerged as a popular framework for improving modeling accuracy while controlling labeling cost. Based on an extension of stochastic composite likelihood we quantify the asymptotic accuracy of generative semi-supervised ...
Balasubramanian, Krishnakumar +2 more
core +1 more source
Semi-Supervised Phoneme Recognition with Recurrent Ladder Networks
, 2017 Ladder networks are a notable new concept in the field of semi-supervised learning by showing state-of-the-art results in image recognition tasks while being compatible with many existing neural architectures.
Alpay, Tayfun +3 more
core +1 more source
Malfustection: Obfuscated Malware Detection and Malware Classification with Data Shortage by Combining Semi-Supervised and Contrastive Learning [PDF]
, 2021 Mohammad Mahdi Maghouli +3 more
openalex +1 more source
Recent Progress on Flexible Multimodal Sensors: Decoupling Strategies, Fabrication and Applications
Advanced Materials, EarlyView.In this review, we establish a tripartite decoupling framework for flexible multimodal sensors, which elucidates the underlying principles of signal crosstalk and their solutions through material design, structural engineering, and AI algorithms. We also demonstrate its potential applications across environmental monitoring, health monitoring, human ...
Tao Wu +10 more
wiley +1 more source
Adversarial Dropout for Supervised and Semi-supervised Learning
, 2017 Recently, the training with adversarial examples, which are generated by adding a small but worst-case perturbation on input examples, has been proved to improve generalization performance of neural networks. In contrast to the individually biased inputs
Moon, Il-Chul +3 more
core +1 more source