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Numerical integration is the task to calculate the numerical value of a definite integral or the numerical solution of differential equations.

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Greatest papers with code

On two ways to use determinantal point processes for Monte Carlo integration

NeurIPS 2019 guilgautier/DPPy

In the absence of DPP machinery to derive an efficient sampler and analyze their estimator, the idea of Monte Carlo integration with DPPs was stored in the cellar of numerical integration.

NUMERICAL INTEGRATION POINT PROCESSES

Hierarchical Deep Learning of Multiscale Differential Equation Time-Steppers

22 Aug 2020luckystarufo/multiscale_HiTS

Our multiscale hierarchical time-stepping scheme provides important advantages over current time-stepping algorithms, including (i) circumventing numerical stiffness due to disparate time-scales, (ii) improved accuracy in comparison with leading neural-network architectures, (iii) efficiency in long-time simulation/forecasting due to explicit training of slow time-scale dynamics, and (iv) a flexible framework that is parallelizable and may be integrated with standard numerical time-stepping algorithms.

NUMERICAL INTEGRATION

Scalable Variational Inference for Dynamical Systems

NeurIPS 2017 ngorbach/Variational_Gradient_Matching_for_Dynamical_Systems

That is why, despite the high computational cost, numerical integration is still the gold standard in many applications.

NUMERICAL INTEGRATION VARIATIONAL INFERENCE

Continuous-in-Depth Neural Networks

5 Aug 2020afqueiruga/ContinuousNet

We first show that ResNets fail to be meaningful dynamical integrators in this richer sense.

NUMERICAL INTEGRATION

Batch Selection for Parallelisation of Bayesian Quadrature

4 Dec 2018OxfordML/bayesquad

Integration over non-negative integrands is a central problem in machine learning (e. g. for model averaging, (hyper-)parameter marginalisation, and computing posterior predictive distributions).

BAYESIAN OPTIMISATION NUMERICAL INTEGRATION

AReS and MaRS - Adversarial and MMD-Minimizing Regression for SDEs

22 Feb 2019gabb7/AReS-MaRS

Stochastic differential equations are an important modeling class in many disciplines.

GAUSSIAN PROCESSES NUMERICAL INTEGRATION

Quadrature-based features for kernel approximation

ICLR 2018 maremun/quffka

We consider the problem of improving kernel approximation via randomized feature maps.

NUMERICAL INTEGRATION

Bayesian inference for logistic models using Polya-Gamma latent variables

2 May 2012zoj613/polya-gamma

We propose a new data-augmentation strategy for fully Bayesian inference in models with binomial likelihoods.

BAYESIAN INFERENCE DATA AUGMENTATION NUMERICAL INTEGRATION

Efficient time stepping for numerical integration using reinforcement learning

8 Apr 2021lueckem/quadrature-ML

To this end, adaptive schemes have been developed that rely on error estimators based on Taylor series expansions.

META-LEARNING NUMERICAL INTEGRATION

i-flow: High-dimensional Integration and Sampling with Normalizing Flows

15 Jan 2020i-flow/i-flow

We introduce the code i-flow, a python package that performs high-dimensional numerical integration utilizing normalizing flows.

NUMERICAL INTEGRATION