The following pages link to (Q5270493):
Displaying 50 items.
- Comparing Two Samples Through Stochastic Dominance: A Graphical Approach (Q107069) (← links)
- The Poisson Multinomial Distribution and Its Applications in Voting Theory, Ecological Inference, and Machine Learning (Q116493) (← links)
- Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations (Q681281) (← links)
- Deep learning observables in computational fluid dynamics (Q777521) (← links)
- Recurrent neural network closure of parametric POD-Galerkin reduced-order models based on the Mori-Zwanzig formalism (Q777576) (← links)
- Controlling oscillations in high-order discontinuous Galerkin schemes using artificial viscosity tuned by neural networks (Q778268) (← links)
- Constraint-aware neural networks for Riemann problems (Q778316) (← links)
- DeepRT: predictable deep learning inference for cyber-physical systems (Q779430) (← links)
- Data driven approximation of parametrized PDEs by reduced basis and neural networks (Q782002) (← links)
- Improved graph-based SFA: information preservation complements the slowness principle (Q782454) (← links)
- Overcoming the curse of dimensionality for some Hamilton-Jacobi partial differential equations via neural network architectures (Q783094) (← links)
- Optimizing optimization: accurate detection of hidden interactions in active body systems from noisy data (Q783431) (← links)
- Parameter identification by statistical learning of a stochastic dynamical system modelling a fishery with price variation (Q784363) (← links)
- Solving forward and inverse problems of the logarithmic nonlinear Schrödinger equation with \(\mathcal{PT}\)-symmetric harmonic potential via deep learning (Q822569) (← links)
- A deep learning algorithm for high-dimensional exploratory item factor analysis (Q823855) (← links)
- Non intrusive reduced order modeling of parametrized PDEs by kernel POD and neural networks (Q825483) (← links)
- Solving high-dimensional Hamilton-Jacobi-Bellman PDEs using neural networks: perspectives from the theory of controlled diffusions and measures on path space (Q825596) (← links)
- Bregman proximal gradient algorithms for deep matrix factorization (Q826170) (← links)
- Mathematical optimization in classification and regression trees (Q828748) (← links)
- A comprehensive deep learning-based approach to reduced order modeling of nonlinear time-dependent parametrized PDEs (Q831238) (← links)
- Federated learning in side-channel analysis (Q831663) (← links)
- Vampire with a brain is a good ITP hammer (Q831938) (← links)
- Learning probabilistic termination proofs (Q832245) (← links)
- Subsampling and knowledge distillation on adversarial examples: new techniques for deep learning based side channel evaluations (Q832386) (← links)
- Deep learning of CMB radiation temperature (Q832575) (← links)
- Deep learning of adaptive control systems based on a logical-probabilistic approach (Q832976) (← links)
- Deep neural networks and mixed integer linear optimization (Q1617390) (← links)
- Deep active inference (Q1627054) (← links)
- Background information of deep learning for structural engineering (Q1639595) (← links)
- On the competition of two conflicting messages (Q1649952) (← links)
- Bayesian inference for spectral projectors of the covariance matrix (Q1657873) (← links)
- A theory of formal synthesis via inductive learning (Q1674868) (← links)
- Deep learning in color: towards automated quark/gluon jet discrimination (Q1678915) (← links)
- An empirical study of testing independencies in Bayesian networks using rp-separation (Q1687290) (← links)
- A machine learning approach for efficient uncertainty quantification using multiscale methods (Q1700748) (← links)
- Evolutionary design of regulatory control. I: A robust control theory analysis of tradeoffs (Q1717071) (← links)
- A neural network method for nonlinear time series analysis (Q1726175) (← links)
- Profiled power analysis attacks using convolutional neural networks with domain knowledge (Q1726696) (← links)
- Mini-workshop: Deep learning and inverse problems. Abstracts from the mini-workshop held March 4--10, 2018 (Q1731979) (← links)
- Applied harmonic analysis and data processing. Abstracts from the workshop held March 25--31, 2018 (Q1731982) (← links)
- The Deep Ritz Method: a deep learning-based numerical algorithm for solving variational problems (Q1744192) (← links)
- On the convergence of formally diverging neural net-based classifiers (Q1747388) (← links)
- Automatic synthesis of constraints from examples using mixed integer linear programming (Q1753666) (← links)
- Neural network closures for nonlinear model order reduction (Q1756917) (← links)
- Explicative deep learning with probabilistic formal concepts in a natural language processing task (Q1789735) (← links)
- Representative datasets for neural networks (Q1792068) (← links)
- Effectiveness analysis of a mixed rumor-quelling strategy (Q1796755) (← links)
- Multi-objective optimization of peel and shear strengths in ultrasonic metal welding using machine learning-based response surface methodology (Q1979603) (← links)
- An immersed boundary neural network for solving elliptic equations with singular forces on arbitrary domains (Q1980025) (← links)
- Extracting Boolean and probabilistic rules from trained neural networks (Q1980413) (← links)
