Results 111 to 120 of about 8,068,470 (297)
Semi-supervised Learning for WLAN Positioning [PDF]
, 2011 Currently the most accurate WLAN positioning systems are based on the fingerprinting approach, where a "radio map" is constructed by modeling how the signal strength measurements vary according to the location. However, collecting a sufficient amount of location-tagged training data is a rather tedious and time consuming task, especially in indoor ...
Teemu Pulkkinen +2 more
openaire +2 more sources
spa: Semi-Supervised Semi-Parametric Graph-Based Estimation in R [PDF]
In this paper, we present an R package that combines feature-based (X) data and graph-based (G) data for prediction of the response Y . In this particular case, Y is observed for a subset of the observations (labeled) and missing for the remainder ...
Mark Culp
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Advanced Materials, EarlyView.Mixed‐cation lead mixed‐halide perovskites suffer from structural instabilities linked to nanoscale heterogeneity. To probe this non‐destructively, a low‐dose, concurrent 4D‐STEM and EDX methodology has been developed. Examining a (FA0.83Cs0.17)Pb(I0.8Br0.2)3 film revealed a complex mosaic of coexisting crystal structures. Crucially, local deficiencies
Jinseok Ryu +6 more
wiley +1 more source
Semi-supervised regression using Hessian energy with an application to semi-supervised dimensionality reduction [PDF]
, 2009 Semi-supervised regression based on the graph Laplacian suffers from the fact that the solution is biased towards a constant and the lack of extrapolating power.
Hein, Matthias +8 more
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Data‐Driven Materials Science for Energy‐Sustainable Applications
Advanced Materials, EarlyView.Data‐driven approaches powered by artificial intelligence are transforming materials discovery for energy sustainability. This review examines how auto‐generated high‐quality materials databases and domain‐specific language models accelerate research in photovoltaics, thermoelectrics, batteries and magnetic materials. Applications involve extraction of
Jacqueline M. Cole
wiley +1 more source
Temporal Ensembling for Semi-Supervised Learning
CoRR, 2016 In this paper, we present a simple and efficient method for training deep neural networks in a semi-supervised setting where only a small portion of training data is labeled. We introduce self-ensembling, where we form a consensus prediction of the unknown labels using the outputs of the network-in-training on different epochs, and most importantly ...
LAINE SAMULI MATIAS, AILA TIMO OSKARI
openaire +4 more sources
Artificial Intelligence Meets Micro/Nanorobotics
Advanced Materials, EarlyView.Artificial intelligence is transforming micro‐ and nanorobots from externally controlled, task‐specific machines into adaptive, autonomous systems. Machine learning, multimodal perception, digital twins, AI‐guided materials and geometry design enhance propulsion, localization, decision‐making, whichaccelerates clinical and environmental applications ...
Fatma M. Yurtsever +6 more
wiley +1 more source
Advanced Materials, EarlyView.Water harvesting, radiative cooling, and interfacial solar evaporation are fundamentally governed by coupled heat, mass, and light transport processes. These processes are mediated by pore architecture, including pore size, connectivity, and hierarchical organization.
Dejan J. Trajkovski +5 more
wiley +1 more source
International Journal of Distributed Sensor Networks, 2017 The research of localization technology based on received signal strength and machine learning has recently attracted a lot of attentions, since with the help of enough labeled training data this technology is able to achieve high positioning accuracy ...
Fagui Liu, Hengrui Qin, Xin Yang, Yi Yu
doaj +1 more source
Learning from Partial Labels with Minimum Entropy [PDF]
This paper introduces the minimum entropy regularizer for learning from partial labels. This learning problem encompasses the semi-supervised setting, where a decision rule is to be learned from labeled and unlabeled examples.
Yoshua Bengio, Yves Grandvalet
core