Results 11 to 20 of about 245,952 (212)
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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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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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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CoRR, 2020 In this paper, we introduce a discrete variant of the meta-learning framework. Meta-learning aims at exploiting prior experience and data to improve performance on future tasks. By now, there exist numerous formulations for meta-learning in the continuous domain.
Arman Adibi +2 more
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CoRR, 2019 Clustering is one of the most fundamental and wide-spread techniques in exploratory data analysis. Yet, the basic approach to clustering has not really changed: a practitioner hand-picks a task-specific clustering loss to optimize and fit the given data to reveal the underlying cluster structure.
Yibo Jiang, Nakul Verma
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, 2019 Meta-learning, or learning to learn, is the science of systematically observing how different machine learning approaches perform on a wide range of learning tasks, and then learning from this experience, or meta-data, to learn new tasks much faster than otherwise possible.
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Meta-learning of Textual Representations [PDF]
, 2020 Recent progress in AutoML has lead to state-of-the-art methods (e.g., AutoSKLearn) that can be readily used by non-experts to approach any supervised learning problem. Whereas these methods are quite effective, they are still limited in the sense that they work for tabular (matrix formatted) data only. This paper describes one step forward in trying to
Jorge G. Madrid +2 more
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Meta Learning for Causal Direction
Proceedings of the AAAI Conference on Artificial Intelligence, 2021 The inaccessibility of controlled randomized trials due to inherent constraints in many fields of science has been a fundamental issue in causal inference. In this paper, we focus on distinguishing the cause from effect in the bivariate setting under limited observational data.
Ton, J-F, Sejdinovic, D, Fukumizu, K
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, 2013 In: Encyclopedia of Systems Biology, W. Dubitzky, O. Wolkenhauer, K-H Cho, H.
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CoRR, 2019 ICML 2019. The first two authors contributed equally.
Chelsea Finn +3 more
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