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Enhanced seam segmentation for automated welding robot in construction through transfer learning: addressing limitations of bilateral segmentation network. [PDF]

Sci Rep
Park K, Voeurn YA, Kweon H, Lee D.
europepmc   +1 more source

Transfer learning for honey bee toxicity prediction: MolFormer versus classical QSAR representations. [PDF]

Ecotoxicology
de Souza Pinho AV, de Alencar Filho EB, de Oliveira Neto RF.   +2 more
europepmc   +1 more source

Video game practice is associated with enhanced value-guided exploitation under probabilistic uncertainty. [PDF]

Psychol Res
Llamas-Alonso LA, Quevedo-Calderon AJ, Quiñones-Beltran SL, Jiménez-Pérez AL, Arvizu-Oviedo A, Angulo-Chavira AQ.   +5 more
europepmc   +1 more source

EvoSNR-Prom: Predicting promoters at single-nucleotide resolution with label-aware transfer learning of the pretrained EVO model. [PDF]

PLoS Comput Biol
Wei PJ, Zheng W, Gu Y, Zheng CH.
europepmc   +1 more source

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A Survey on Transfer Learning

IEEE Transactions on Knowledge and Data Engineering, 2010
A major assumption in many machine learning and data mining algorithms is that the training and future data must be in the same feature space and have the same distribution. However, in many real-world applications, this assumption may not hold. For example, we sometimes have a classification task in one domain of interest, but we only have sufficient ...
Yang Qiang, Sinno Jialin Pan
exaly   +4 more sources

Transfer Learning by Kernel Meta-Learning.

, 2012
A crucial issue in machine learning is how to learn appropriate representations for data. Recently, much work has been devoted to kernel learning, that is, the problem of finding a good kernel matrix for a given task. This can be done in a semi-supervised learning setting by using a large set of unlabeled data and a (typically small) set of i.i.d ...
AIOLLI, FABIO
openaire   +3 more sources