Results 101 to 110 of about 131,851 (273)
ACHO: Adaptive Conformal Hyperparameter Optimization
, 2022 Several novel frameworks for hyperparameter search have emerged in the last decade, but most rely on strict, often normal, distributional assumptions, limiting search model flexibility. This paper proposes a novel optimization framework based on upper confidence bound sampling of conformal confidence intervals, whose weaker assumption of ...
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Causal Prediction of TP53 Variant Pathogenicity Using a Perturbation‐Informed Protein Language Model
Advanced Science, EarlyView.A TP53‐specific predictor, CaVepP53, is developed by fine‐tuning ESMC on experimentally validated variants, quantifying pathogenicity via Euclidean distances. It outperforms general‐purpose models and extends to five cancer genes, enabling interpretable variant classification for precision medicine.
Huiying Chen +15 more
wiley +1 more source
Overtuning in Hyperparameter Optimization
Accepted at the Fourth Conference on Automated Machine Learning (Methods Track).
Schneider, Lennart +2 more
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Hyperparameters: Optimize, or Integrate Out? [PDF]
, 1996 I examine two approximate methods for computational implementation of Bayesian hierarchical models, that is, models which include unknown hyperparameters such as regularization constants. In the ‘evidence framework’ the model parameters are integrated over, and the resulting evidence is maximized over the hyperparameters.
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Advanced Science, EarlyView.Geometry and connectivity are complementary structures, which have demonstrated their ability to represent the brain's functional activity. This study evaluates geometric and connectome eigenmodes as biologically informed constraints for EEG source localization.
Pok Him Siu +6 more
wiley +1 more source
A two-stage renal disease classification based on transfer learning with hyperparameters optimization. [PDF]
Front Med (Lausanne), 2023 Badawy M +5 more
europepmc +1 more source
Hyperparameter Optimization Across Problem Tasks
, 2018 Hyperparameter Optimization is a task that is generally hard to accomplish as the correct setting of hyperparameters cannot be learned from the data directly. However, finding the right hyperparameters is necessary as the performance on test data can differ a lot under various hyperparameter settings.
Schilling, Nicolas +2 more
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Machine Learning for Green Solvents: Assessment, Selection and Substitution
Advanced Science, EarlyView.Environmental regulations have intensified demand for green solvents, but discovery is limited by Solvent Selection Guides (SSGs) that quantify solvent sustainability. Training a machine learning model on GlaxoSmithKline SSG, a database of sustainability metrics for 10,189 solvents, GreenSolventDB is developed. Integrated with Hansen solubility metrics,
Rohan Datta +4 more
wiley +1 more source
Solid Harmonic Wavelet Bispectrum for Image Analysis
Advanced Science, EarlyView.The Solid Harmonic Wavelet Bispectrum (SHWB), a rotation‐ and translation‐invariant descriptor that captures higher‐order (phase) correlations in signals, is introduced. Combining wavelet scattering, bispectral analysis, and group theory, SHWB achieves interpretable, data‐efficient representations and demonstrates competitive performance across texture,
Alex Brown +3 more
wiley +1 more source
Machine Learning Hyperparameters Optimization for Accurate Arabic Sentiment Classification
Proceedings of the International Conference on Applied Innovations in ITAn improved model performance is achieved by optimizing hyperparameters for Arabic sentiment classification based on machine learning. The use of RNNs, LSTMs, and GRUs, as well as Logistic Regression, Random Forests, and Support Vector Machines as meta ...
Irwan Lakawa +2 more
doaj +1 more source