Results 211 to 220 of about 295,561 (260)

Thermodynamic Limits to Molecular Doping in Conjugated Polymers: A Perspective on Phase Behavior and Miscibility

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, Justin Neu, Sung‐Joo Kwon, Runqio Song, Zixuan Chen, Tajah Trapier, Paschalis Gkoupidenis, David Ginger, Wei You, Jasper J. Michels, Harald Ade   +10 more
wiley   +1 more source

Accurate Asthma-COPD Overlap Classification via Deep Transfer Learning in Medical Image Segmentation. [PDF]

Int J Chron Obstruct Pulmon Dis
Ye W, Mo D, Yang Y.
europepmc   +1 more source

Correction to "Automated Guava Disease Detection Using Transfer Learning With ResNet-101". [PDF]

Food Sci Nutr
europepmc   +1 more source

Tissue-specific transfer learning improves functional variant and therapeutic target discoveries in breast and prostate cancer. [PDF]

PLoS Genet
Li Q, Wang D, Zhang Z, Perera D, Chen Z, Wen W, MacDonald ME, Cai W, Yan J, Shu XO, Zheng W, Guo X, Long Q.   +12 more
europepmc   +1 more source

Genetic transfer learning

Expert Systems With Applications, 2010
Transfer learning is a method which aims to improve ''related'' tasks performance. Transfer learning tries to use information gained from related tasks solutions to improve performance of learning strategy. Transfer learning addresses the problem of how to utilize plenty of labeled data in a source domain to solve related but different problems in a ...
Baris Kocer, Ahmet Arslan
exaly   +3 more sources

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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 ...
Sinno Jialin Pan, Yang Qiang
exaly   +3 more sources