The following pages link to (Q5405117):
Displaying 50 items.
- Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization (Q72746) (← links)
- Algorithm runtime prediction: methods \& evaluation (Q490455) (← links)
- Learning an efficient constructive sampler for graphs (Q511780) (← links)
- A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems (Q776737) (← links)
- Subsampling bias and the best-discrepancy systematic cross validation (Q829119) (← links)
- Random drift particle swarm optimization algorithm: convergence analysis and parameter selection (Q890323) (← links)
- Importance of search-domain reduction in random optimization (Q1321281) (← links)
- A search grid for parameter optimization as a byproduct of model sensitivity analysis (Q1643267) (← links)
- Agent-based model calibration using machine learning surrogates (Q1657336) (← links)
- Improved support vector machine algorithm for heterogeneous data (Q1678705) (← links)
- Fast Bayesian hyperparameter optimization on large datasets (Q1688974) (← links)
- Scalable Gaussian process-based transfer surrogates for hyperparameter optimization (Q1707465) (← links)
- On the estimation of Pareto fronts from the point of view of copula theory (Q1750061) (← links)
- Multilayered neural architectures evolution for computing sequences of orthogonal polynomials (Q1757452) (← links)
- Hyperparameter optimization in learning systems (Q1983037) (← links)
- Machine learning materials physics: surrogate optimization and multi-fidelity algorithms predict precipitate morphology in an alternative to phase field dynamics (Q1986728) (← links)
- Complete mechanical regularization applied to digital image and volume correlation (Q1988198) (← links)
- Design optimization under uncertainties of a mesoscale implant in biological tissues using a probabilistic learning algorithm (Q1990872) (← links)
- A comparative study of the leading machine learning techniques and two new optimization algorithms (Q1991232) (← links)
- A data-driven newsvendor problem: from data to decision (Q1999637) (← links)
- A generic physics-informed neural network-based constitutive model for soft biological tissues (Q2021025) (← links)
- Resolving learning rates adaptively by locating stochastic non-negative associated gradient projection points using line searches (Q2022225) (← links)
- Optimization problems for machine learning: a survey (Q2029894) (← links)
- Data-driven simulation for general-purpose multibody dynamics using deep neural networks (Q2034108) (← links)
- Dataset2Vec: learning dataset meta-features (Q2036744) (← links)
- Handling imbalance in hierarchical classification problems using local classifiers approaches (Q2036779) (← links)
- Data-driven algorithm selection and tuning in optimization and signal processing (Q2043447) (← links)
- Surrogate optimization of deep neural networks for groundwater predictions (Q2046338) (← links)
- MODES: model-based optimization on distributed embedded systems (Q2051343) (← links)
- Automated data-driven selection of the hyperparameters for total-variation-based texture segmentation (Q2051545) (← links)
- Multi-fidelity meta modeling using composite neural network with online adaptive basis technique (Q2060166) (← links)
- MultiETSC: automated machine learning for early time series classification (Q2066661) (← links)
- Efficient well placement optimization under uncertainty using a virtual drilling procedure (Q2085041) (← links)
- A taxonomy of weight learning methods for statistical relational learning (Q2102343) (← links)
- Optimised one-class classification performance (Q2102347) (← links)
- Estimating transfer fees of professional footballers using advanced performance metrics and machine learning (Q2106754) (← links)
- Automatic model training under restrictive time constraints (Q2108929) (← links)
- Generalized hierarchical expected improvement method based on black-box functions of adaptive search strategy (Q2109428) (← links)
- One-shot learning of stochastic differential equations with data adapted kernels (Q2111726) (← links)
- Four algorithms to solve symmetric multi-type non-negative matrix tri-factorization problem (Q2114581) (← links)
- Product optimization in stepwise design (Q2115903) (← links)
- Use of static surrogates in hyperparameter optimization (Q2120124) (← links)
- \(\ell_2\)-penalized approximate likelihood inference in logit mixed models for regional prevalence estimation under covariate rank-deficiency (Q2124786) (← links)
- Symbolic DNN-tuner (Q2127252) (← links)
- Machine learning for fluid flow reconstruction from limited measurements (Q2134510) (← links)
- Prediction of hereditary cancers using neural networks (Q2135373) (← links)
- Automated porosity estimation using CT-scans of extracted core data (Q2147571) (← links)
- A one-bit, comparison-based gradient estimator (Q2155805) (← links)
- Comprehensive analysis of gradient-based hyperparameter optimization algorithms (Q2158645) (← links)
- Estimating shape parameters of piecewise linear-quadratic problems (Q2165586) (← links)
