tiskw / random-fourier-featuresLinks
Implementation of random Fourier features for kernel method, like support vector machine and Gaussian process model
☆101Updated 9 months ago
Alternatives and similar repositories for random-fourier-features
Users that are interested in random-fourier-features are comparing it to the libraries listed below
Sorting:
- Implementation of Unconstrained Monotonic Neural Network and the related experiments. These architectures are particularly useful for mod…☆123Updated 7 months ago
- Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances☆47Updated 2 years ago
- Code for the paper "Bayesian Neural Network Priors Revisited"☆57Updated 4 years ago
- Code for random Fourier features based on Rahimi and Recht's 2007 paper.☆56Updated 4 years ago
- ☆151Updated 2 years ago
- Non-stationary spectral mixture kernels implemented in GPflow☆28Updated 6 years ago
- Reference implementation of variational sequential Monte Carlo proposed by Naesseth et al. "Variational Sequential Monte Carlo" (2018)☆65Updated 6 years ago
- Code Repo for "Subspace Inference for Bayesian Deep Learning"☆82Updated last year
- Code for "Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations"☆171Updated 3 years ago
- A community repository for benchmarking Bayesian methods☆110Updated 3 years ago
- Bayesian Deep Learning with Stochastic Gradient MCMC Methods☆39Updated 3 years ago
- Light-weighted code for Orthogonal Additive Gaussian Processes☆43Updated last year
- 🤿 Implementation of doubly stochastic deep Gaussian Process using GPflow and TensorFlow 2.0☆26Updated last year
- Random Fourier Features☆49Updated 8 years ago
- Code repo for "Function-Space Distributions over Kernels"☆32Updated 4 years ago
- Code for: "Neural Rough Differential Equations for Long Time Series", (ICML 2021)☆118Updated 4 years ago
- Demos for the paper Generalized Variational Inference (Knoblauch, Jewson & Damoulas, 2019)☆20Updated 6 years ago
- Official code for the ICLR 2021 paper Neural ODE Processes☆73Updated 3 years ago
- Differentiable ODE solvers with full GPU support and O(1)-memory backpropagation.☆61Updated 4 years ago
- Implementation for Non-stationary Spectral Kernels (NIPS 2017)☆20Updated 5 years ago
- Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)☆48Updated 5 years ago
- Sequential Neural Likelihood☆40Updated 5 years ago
- AISTATS paper 'Uncertainty in Neural Networks: Approximately Bayesian Ensembling'☆89Updated 4 years ago
- Heterogeneous Multi-output Gaussian Processes☆52Updated 5 years ago
- Differentiable computations for the signature-PDE-kernel on CPU and GPU.☆55Updated last year
- Package implementing various parametric and nonparametric methods for conditional density estimation☆196Updated 2 years ago
- Code for efficiently sampling functions from GP(flow) posteriors☆72Updated 4 years ago
- Bayes-Newton—A Gaussian process library in JAX, with a unifying view of approximate Bayesian inference as variants of Newton's method.☆234Updated last year
- code for the paper "Stein Variational Gradient Descent (SVGD): A General Purpose Bayesian Inference Algorithm"☆100Updated 6 years ago
- Code for the Neural Processes website and replication of 4 papers on NPs. Pytorch implementation.☆223Updated last year