Results 101 to 110 of about 8,068,470 (297)
Efficient Non-Parametric Function Induction in Semi-Supervised Learning [PDF]
There has been an increase of interest for semi-supervised learning recently, because of the many datasets with large amounts of unlabeled examples and only a few labeled ones.
Yoshua Bengio +2 more
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Unlabeled pattern management through Semi-Supervised classification techniques [PDF]
, 2022 l'obbiettivo di questo progetto consiste nell'analizzare le performance di alcuni algoritmi di semi-supervised learning proposti negli ultimi anni. In particolare si è usato un algoritmo di feature selection basato su Self-training per determinare l ...
Segato, Giordano
core
Advanced Materials, EarlyView.Molecular doping of conjugated polymers is fundamentally constrained by thermodynamic phase behavior. This Perspective reframes doping efficiency and stability in terms of miscibility limits, binodals, and solvus boundaries, highlighting the role of effective interaction parameters and charge transfer.
Somayeh Kashani +10 more
wiley +1 more source
Negative sampling in semi-supervised learning
CoRR, 2019 We introduce Negative Sampling in Semi-Supervised Learning (NS3L), a simple, fast, easy to tune algorithm for semi-supervised learning (SSL). NS3L is motivated by the success of negative sampling/contrastive estimation. We demonstrate that adding the NS3L loss to state-of-the-art SSL algorithms, such as the Virtual Adversarial Training (VAT ...
John Chen 0002 +2 more
openaire +4 more sources
A Taxonomy for Semi-Supervised Learning Methods [PDF]
We propose a simple taxonomy of probabilistic graphical models for the semi-supervised learning problem. We give some broad classes of algorithms for each of the families and point to specific realizations in the literature.
Seeger, Matthias
core +1 more source
Organic Materials of Tomorrow: Horizons of Artificial Intelligence
Advanced Materials, EarlyView.This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena +3 more
wiley +1 more source
Multiview Semi-Supervised Learning with Consensus
IEEE Transactions on Knowledge and Data Engineering, 2012 Obtaining high-quality and up-to-date labeled data can be difficult in many real-world machine learning applications. Semi-supervised learning aims to improve the performance of a classifier trained with limited number of labeled data by utilizing the unlabeled ones.
Guangxia Li +2 more
openaire +4 more sources
“Smelltronics”—From Gas to Smell Sensing
Advanced Materials, EarlyView.The emerging field of smelltronics, encompassing sensing technologies for complex volatile organic compounds, holds significant potential for extracting valuable chemical information. It facilitates the noninvasive, real‐time monitoring of humans, food, and the environment.
Takeshi Ono +7 more
wiley +1 more source
Ensemble Knowledge Distillation for Federated Semi-Supervised Image Classification
Tsinghua Science and TechnologyFederated learning is an emerging privacy-preserving distributed learning paradigm, in which many clients collaboratively train a shared global model under the orchestration of a remote server.
Ertong Shang +4 more
doaj +1 more source
Semi-Supervised Learning with Ladder Networks
CoRR, 2015 We combine supervised learning with unsupervised learning in deep neural networks. The proposed model is trained to simultaneously minimize the sum of supervised and unsupervised cost functions by backpropagation, avoiding the need for layer-wise pre-training.
Antti Rasmus +4 more
openaire +4 more sources