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Universal Metric Learning with Parameter-Efficient Transfer Learning
2023A common practice in metric learning is to train and test an embedding model for each dataset. This dataset-specific approach fails to simulate real-world scenarios that involve multiple heterogeneous distributions of data. In this regard, we introduce a novel metric learning paradigm, called Universal Metric Learning (UML), which learns a unified ...
Kim, Sungyeon, Kim, Donghyun, Kwak, Suha
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Optimizing Generalized Motor Program and Parameter Learning
Research Quarterly for Exercise and Sport, 2000Two experiments examined generalized motor program (GMP) and parameter learning. Experiment 1 examined the effects of bandwidth knowledge of results (KR) about relative timing in constant and variable practice. The purpose was to determine if movement stability created by the bandwidth manipulation is associated with increased GMP learning and if ...
Lai, Q., Shea, C., Wulf, G., Wright, D.
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STATISTICAL LEARNING WITH TIME-VARYING PARAMETERS
Macroeconomic Dynamics, 2003In their landmark paper, Bray and Savin note that the constant-parameters model used by their agents to form expectations is misspecified and that, using standard econometric techniques, agents may be able to determine the time-varying nature of the model's parameters.
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Molecular Parameters in Memory and Learning
1966Great biological concepts in the past have not been of the component, molecular type, but rather have been of the holistic, systems type (for example, those of Darwin, Freud, Pavlov). With the expanding scope of the physics and chemistry of large biomolecules (macromolecules) has emerged a new field, “molecular biology,” which ranks conceptually in ...
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Bayesian Learning and Evolutionary Parameter Optimization
2001Summary: I want to argue that the combination of evolutionary algorithms and neural networks can be fruitful in several ways. When estimating a functional relationship on the basis of empirical data we face three basic problems. Firstly, we have to deal with noisy and finite-sized data sets which is usually done be regularization techniques, for ...
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Machine learning for microbiologists
Nature Reviews Microbiology, 2023Francesco Asnicar +2 more
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Machine learning methods to model multicellular complexity and tissue specificity
Nature Reviews Materials, 2021Aaron K Wong, Olga G Troyanskaya
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Machine learning sheds light on microbial dark proteins
Nature Reviews Microbiology, 2023Aaron T Hammack, Crysten E Blaby-Haas
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