Task‐Similarity is a Crucial Factor for Few‐Shot Meta‐Learning of Structure‐Activity Relationships
Sign Up to like & getrecommendations! Published in 2024 at "ChemBioChem"
DOI: 10.1002/cbic.202400095
Abstract: Machine learning models support computer‐aided molecular design and compound optimization. However, the initial phases of drug discovery often face a scarcity of training data for these models. Meta‐learning has emerged as a potentially promising strategy,… read more here.
Keywords: structure activity; meta learning; similarity;
Meta Learning for Improved Neural Network Wavefield Solutions
Sign Up to like & getrecommendations! Published in 2024 at "Surveys in Geophysics"
DOI: 10.1007/s10712-024-09872-6
Abstract: Physics-informed neural networks (PINNs) provide a flexible and effective alternative for estimating seismic wavefield solutions due to their typical mesh-free and unsupervised features. However, their accuracy and training cost restrict their applicability. To address these… read more here.
Keywords: meta learning; neural network; learning improved; network ... See more keywords
Few-shot learning with adaptively initialized task optimizer: a practical meta-learning approach
Sign Up to like & getrecommendations! Published in 2019 at "Machine Learning"
DOI: 10.1007/s10994-019-05838-7
Abstract: Considering the data collection and labeling cost in real-world applications, training a model with limited examples is an essential problem in machine learning, visual recognition, etc. Directly training a model on such few-shot learning (FSL)… read more here.
Keywords: shot learning; meta learning; task; adaptively initialized ... See more keywords
Meta-learning in natural and artificial intelligence
Sign Up to like & getrecommendations! Published in 2021 at "Current Opinion in Behavioral Sciences"
DOI: 10.1016/j.cobeha.2021.01.002
Abstract: Meta-learning, or learning to learn, has gained renewed interest in recent years within the artificial intelligence community. However, meta-learning is incredibly prevalent within nature, has deep roots in cognitive science and psychology, and is currently… read more here.
Keywords: intelligence; meta learning; artificial intelligence; learning natural ... See more keywords
Serotonin neurons modulate learning rate through uncertainty
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DOI: 10.1016/j.cub.2021.12.006
Abstract: Regulating how fast to learn is critical for flexible behavior. Learning about the consequences of actions should be slow in stable environments, but accelerate when that environment changes. Recognizing stability and detecting change are difficult… read more here.
Keywords: modulate learning; meta learning; serotonin; serotonin neurons ... See more keywords
Emotion recognition from facial images with simultaneous occlusion, pose and illumination variations using meta-learning
Sign Up to like & getrecommendations! Published in 2021 at "Journal of King Saud University - Computer and Information Sciences"
DOI: 10.1016/j.jksuci.2021.06.012
Abstract: Abstract Automatic facial emotion recognition in real-world situations like partial occlusions, varying head poses and illumination conditions are challenging to the machine learning community. The main reason is the lack of sufficient samples with the… read more here.
Keywords: illumination; emotion recognition; meta learning;
A Concise Review of Recent Few-shot Meta-learning Methods
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DOI: 10.1016/j.neucom.2020.05.114
Abstract: Few-shot meta-learning has been recently reviving with expectations to mimic humanity's fast adaption to new concepts based on prior knowledge. In this short communication, we give a concise review on recent representative methods in few-shot… read more here.
Keywords: concise review; shot meta; meta learning; review recent ... See more keywords
Context-sensitive zero-shot semantic segmentation model based on meta-learning
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DOI: 10.1016/j.neucom.2021.08.120
Abstract: Abstract The zero-shot semantic segmentation requires models with a strong image understanding ability. The majority of current solutions are based on direct mapping or generation. These schemes are effective in dealing with the zero-shot recognition,… read more here.
Keywords: shot semantic; meta learning; shot; zero shot ... See more keywords
MetaMBP: Few-Shot Multilabel Prediction of Bioactive Peptides Based on Deep Metric Meta-Learning
Sign Up to like & getrecommendations! Published in 2025 at "Journal of chemical information and modeling"
DOI: 10.1021/acs.jcim.5c01309
Abstract: Bioactive peptides are highly specific and have low toxicity, making them a promising treatment option. There are many different types of bioactive peptides, while some types have limited samples (under 500). Methods that can handle… read more here.
Keywords: based deep; bioactive peptides; deep metric; metric meta ... See more keywords
A meta-learning approach for genomic survival analysis
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DOI: 10.1038/s41467-020-20167-3
Abstract: RNA sequencing has emerged as a promising approach in cancer prognosis as sequencing data becomes more easily and affordably accessible. However, it remains challenging to build good predictive models especially when the sample size is… read more here.
Keywords: cancer; survival analysis; meta learning; learning approach ... See more keywords
A meta-learning framework to mitigate negative transfer in transfer learning applicable to drug design
Sign Up to like & getrecommendations! Published in 2025 at "Scientific Reports"
DOI: 10.1038/s41598-025-22058-3
Abstract: Data sparseness is a major limiting factor for deep machine learning. In the natural sciences, data distributions are heterogeneous. For instance, in chemistry and early-phase drug discovery, compound and molecular property data are typically sparse… read more here.
Keywords: transfer learning; meta learning; drug; transfer ... See more keywords