Results 21 to 30 of about 7,740,063 (200)
Meta-learned models of cognition
Behavioral and Brain Sciences, 2023 Abstract Psychologists and neuroscientists extensively rely on computational models for studying and analyzing the human mind. Traditionally, such computational models have been hand-designed by expert researchers. Two prominent examples are cognitive architectures and Bayesian models of cognition.
Marcel Binz +5 more
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Meta-Learning Initializations for Low-Resource Drug Discovery [PDF]
, 2020 Building in silico models to predict chemical properties and activities is a crucial step in drug discovery. However, drug discovery projects are often characterized by limited labeled data, hindering the applications of deep learning in this setting ...
Constantine, Kreatsoulas +2 more
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CoRR, 2019 Meta learning is a promising solution to few-shot learning problems. However, existing meta learning methods are restricted to the scenarios where training and application tasks share the same out-put structure. To obtain a meta model applicable to the tasks with new structures, it is required to collect new training data and repeat the time-consuming ...
Yingtian Zou, Jiashi Feng
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CoRR, 2020 Meta-learning leverages related source tasks to learn an initialization that can be quickly fine-tuned to a target task with limited labeled examples. However, many popular meta-learning algorithms, such as model-agnostic meta-learning (MAML), only assume access to the target samples for fine-tuning.
Diana Cai +3 more
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A Contrastive Rule for Meta-Learning
Advances in Neural Information Processing Systems 35, 2022 Meta-learning algorithms leverage regularities that are present on a set of tasks to speed up and improve the performance of a subsidiary learning process. Recent work on deep neural networks has shown that prior gradient-based learning of meta-parameters can greatly improve the efficiency of subsequent learning.
Zucchet, Nicolas +4 more
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Federated meta learning: a review
大数据, 2023 With the popularity of mobile devices, massive amounts of data are constantly produced.The data privacy policies are becoming more and more specified, the flow and use of data are strictly regulated.Federated learning can break data barriers and use ...
Chuanyao ZHANG +3 more
doaj
Meta-learning with Network Pruning [PDF]
, 2020 Meta-learning is a powerful paradigm for few-shot learning. Although with remarkable success witnessed in many applications, the existing optimization based meta-learning models with over-parameterized neural networks have been evidenced to ovetfit on training tasks.
Hongduan Tian +3 more
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Towards Explainable Meta-learning [PDF]
, 2021 Meta-learning is a field that aims at discovering how different machine learning algorithms perform on a wide range of predictive tasks. Such knowledge speeds up the hyperparameter tuning or feature engineering. With the use of surrogate models various aspects of the predictive task such as meta-features, landmarker models e.t.c.
Katarzyna Woznica, Przemyslaw Biecek
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