The following pages link to Spearmint (Q29711):
Displaying 50 items.
- Physics-informed neural networks for high-speed flows (Q2175317) (← links)
- Optimizing predictive precision in imbalanced datasets for actionable revenue change prediction (Q2184072) (← links)
- A stochastic variational framework for recurrent Gaussian processes models (Q2188216) (← links)
- Large-scale Gaussian process inference with generalized histogram intersection kernels for visual recognition tasks (Q2193880) (← links)
- Generalized exponential autoregressive models for nonlinear time series: stationarity, estimation and applications (Q2195454) (← links)
- A Bayesian perspective of statistical machine learning for big data (Q2203387) (← links)
- Optimal observations-based retrieval of topography in 2D shallow water equations using PC-EnKF (Q2214574) (← links)
- Data-driven polynomial chaos expansion for machine learning regression (Q2220634) (← links)
- Detecting troubled-cells on two-dimensional unstructured grids using a neural network (Q2222514) (← links)
- Improved outcome prediction across data sources through robust parameter tuning (Q2236766) (← links)
- Prediction and identification of physical systems by means of physically-guided neural networks with meaningful internal layers (Q2236964) (← links)
- Directed particle swarm optimization with Gaussian-process-based function forecasting (Q2239855) (← links)
- A novel hybrid PSO-based metaheuristic for costly portfolio selection problems (Q2241553) (← links)
- Leveraged least trimmed absolute deviations (Q2241912) (← links)
- A virtual model architecture for engineering structures with twin extended support vector regression (T-X-SVR) method (Q2246326) (← links)
- General solutions for nonlinear differential equations: a rule-based self-learning approach using deep reinforcement learning (Q2281483) (← links)
- Scalable high-resolution forecasting of sparse spatiotemporal events with kernel methods: a winning solution to the NIJ ``Real-time crime forecasting challenge'' (Q2291539) (← links)
- Certifiably optimal sparse principal component analysis (Q2293653) (← links)
- High-dimensional Bayesian optimization with projections using quantile Gaussian processes (Q2300637) (← links)
- A modern retrospective on probabilistic numerics (Q2302460) (← links)
- Local dimension reduction of summary statistics for likelihood-free inference (Q2302508) (← links)
- Combining Bayesian optimization and Lipschitz optimization (Q2303661) (← links)
- Variational approach for learning Markov processes from time series data (Q2303757) (← links)
- Hyper-parameter optimization for support vector machines using stochastic gradient descent and dual coordinate descent (Q2308188) (← links)
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations (Q2314336) (← links)
- Deep collective matrix factorization for augmented multi-view learning (Q2320570) (← links)
- The algorithm selection competitions 2015 and 2017 (Q2321293) (← links)
- Quantifying uncertainties in first-principles alloy thermodynamics using cluster expansions (Q2424411) (← links)
- An adaptive Bayesian approach to surrogate-assisted evolutionary multi-objective optimization (Q2660961) (← links)
- Learning nonlinear state-space models using autoencoders (Q2665158) (← links)
- Improved complexities for stochastic conditional gradient methods under interpolation-like conditions (Q2670499) (← links)
- CMD: controllable matrix decomposition with global optimization for deep neural network compression (Q2673311) (← links)
- Bayesian optimization with partially specified queries (Q2673324) (← links)
- Data driven modeling of interfacial traction-separation relations using a thermodynamically consistent neural network (Q2678537) (← links)
- Prediction of permeability of porous media using optimized convolutional neural networks (Q2683510) (← links)
- A deep learning model to predict the failure response of steel pipes under pitting corrosion (Q2692887) (← links)
- Multi-fidelity cost-aware Bayesian optimization (Q2693418) (← links)
- A cluster and search stacking algorithm (CSSA) for predicting the ultimate bearing capacity of an HSS column (Q2694714) (← links)
- Zeroth-order nonconvex stochastic optimization: handling constraints, high dimensionality, and saddle points (Q2696568) (← links)
- Bayesian optimization for likelihood-free inference of simulator-based statistical models (Q2834439) (← links)
- A general framework for constrained Bayesian optimization using information-based search (Q2834491) (← links)
- Bilevel Optimization with Nonsmooth Lower Level Problems (Q3300346) (← links)
- Accurate prediction of the particle image velocimetry flow field and rotor thrust using deep learning (Q3390379) (← links)
- Gradient-based Regularization Parameter Selection for Problems With Nonsmooth Penalty Functions (Q3391123) (← links)
- Practical Bayesian support vector regression for financial time series prediction and market condition change detection (Q4555150) (← links)
- (Q4558158) (← links)
- (Q4558177) (← links)
- Neural Networks and Deep Learning (Q4569250) (← links)
- (Q4637023) (← links)
- (Q4637069) (← links)
