The following pages link to Scikit (Q20073):
Displaying 50 items.
- Non-technical losses detection in energy consumption focusing on energy recovery and explainability (Q2127244) (← links)
- Multi-target prediction for dummies using two-branch neural networks (Q2127254) (← links)
- Physically interpretable machine learning algorithm on multidimensional non-linear fields (Q2128352) (← links)
- A comparative study of machine learning models for predicting the state of reactive mixing (Q2128488) (← links)
- A dictionary learning algorithm for compression and reconstruction of streaming data in preset order (Q2129137) (← links)
- Acceleration of thermodynamic computations in fluid flow applications (Q2130941) (← links)
- Side-constrained minimum sum-of-squares clustering: mathematical programming and random projections (Q2131141) (← links)
- Development and analysis of a sentence semantics representation model (Q2132072) (← links)
- Quantitative analysis of the kinematics and induced aerodynamic loading of individual vortices in vortex-dominated flows: a computation and data-driven approach (Q2132584) (← links)
- Deep-learning accelerated calculation of real-fluid properties in numerical simulation of complex flowfields (Q2132659) (← links)
- Prediction of magnetization dynamics in a reduced dimensional feature space setting utilizing a low-rank kernel method (Q2132679) (← links)
- Quantum kernels with Gaussian state encoding for machine learning (Q2133131) (← links)
- Data-driven fractional subgrid-scale modeling for scalar turbulence: a nonlocal LES approach (Q2133512) (← links)
- INK: knowledge graph embeddings for node classification (Q2134043) (← links)
- Machine learning for fluid flow reconstruction from limited measurements (Q2134510) (← links)
- RotEqNet: rotation-equivariant network for fluid systems with symmetric high-order tensors (Q2138017) (← links)
- Constructing motion primitive sets to summarize periodic orbit families and hyperbolic invariant manifolds in a multi-body system (Q2138477) (← links)
- Adaptivity for clustering-based reduced-order modeling of localized history-dependent phenomena (Q2138765) (← links)
- Ordinal synchronization and typical states in high-frequency digital markets (Q2139969) (← links)
- Clustering as a dual problem to colouring (Q2140780) (← links)
- Seven principles for rapid-response data science: lessons learned from COVID-19 forecasting (Q2143953) (← links)
- Uncertainty-aware resampling method for imbalanced classification using evidence theory (Q2146035) (← links)
- A data-driven, variable-speed model for the train timetable rescheduling problem (Q2146975) (← links)
- Combining mathematical modeling and deep learning to make rapid and explainable predictions of the patient-specific response to anticoagulant therapy under venous flow (Q2147444) (← links)
- Mapping natural fracture networks using geomechanical inferences from machine learning approaches (Q2147575) (← links)
- Obey validity limits of data-driven models through topological data analysis and one-class classification (Q2147924) (← links)
- The signed cumulative distribution transform for 1-D signal analysis and classification (Q2148959) (← links)
- Importance of numerical implementation and clustering analysis in force-directed algorithms for accurate community detection (Q2152709) (← links)
- Self-triggered control of probabilistic Boolean control networks: a reinforcement learning approach (Q2159969) (← links)
- Scrutinizing XAI using linear ground-truth data with suppressor variables (Q2163233) (← links)
- The backbone method for ultra-high dimensional sparse machine learning (Q2163249) (← links)
- Probabilistic predictions of SIS epidemics on networks based on population-level observations (Q2164661) (← links)
- On the implementation of a global optimization method for mixed-variable problems (Q2165595) (← links)
- Buckling of functionally graded hydrogen-functionalized graphene reinforced beams based on machine learning-assisted micromechanics models (Q2168389) (← links)
- Training thinner and deeper neural networks: jumpstart regularization (Q2170213) (← links)
- Brain webs for brane webs (Q2172314) (← links)
- Conclusive local interpretation rules for random forests (Q2172632) (← links)
- Robust statistical learning with Lipschitz and convex loss functions (Q2174664) (← links)
- Identification of input random field samples causing extreme responses (Q2174750) (← links)
- Bayesian inference of non-linear multiscale model parameters accelerated by a deep neural network (Q2175257) (← links)
- Bayesian model-scenario averaged predictions of compressor cascade flows under uncertain turbulence models (Q2176864) (← links)
- Strategic offering of a flexible producer in day-ahead and intraday power markets (Q2178147) (← links)
- Graph based analysis for gene segment organization in a scrambled genome (Q2180788) (← links)
- CrystalBall: gazing in the black box of SAT solving (Q2181946) (← links)
- A discontinuous derivative-free optimization framework for multi-enterprise supply chain (Q2182779) (← links)
- Principled analytic classifier for positive-unlabeled learning via weighted integral probability metric (Q2183591) (← links)
- Optimal arrangements of hyperplanes for SVM-based multiclass classification (Q2183661) (← links)
- Optimizing predictive precision in imbalanced datasets for actionable revenue change prediction (Q2184072) (← links)
- Time-series machine-learning error models for approximate solutions to parameterized dynamical systems (Q2184303) (← links)
- Uncertainty propagation in reduced order models based on crystal plasticity (Q2184329) (← links)
