tiskw / random-fourier-features
Implementation of random Fourier features for kernel method, like support vector machine and Gaussian process model
☆100Updated 4 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
- Code for random Fourier features based on Rahimi and Recht's 2007 paper.☆52Updated 4 years ago
- Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances☆46Updated last year
- Light-weighted code for Orthogonal Additive Gaussian Processes☆41Updated 7 months ago
- Code for "Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations"☆169Updated 3 years ago
- Implementation of Unconstrained Monotonic Neural Network and the related experiments. These architectures are particularly useful for mod…☆118Updated 3 months ago
- Non-stationary spectral mixture kernels implemented in GPflow☆28Updated 6 years ago
- Sampling with gradient-based Markov Chain Monte Carlo approaches☆97Updated 11 months ago
- ☆150Updated 2 years ago
- Differentiable computations for the signature-PDE-kernel on CPU and GPU.☆53Updated 10 months ago
- Differentiable ODE solvers with full GPU support and O(1)-memory backpropagation.☆58Updated 4 years ago
- Random Fourier Features☆50Updated 7 years ago
- Official code for the ICLR 2021 paper Neural ODE Processes☆72Updated 2 years ago
- Lightweight library of stochastic gradient MCMC algorithms written in JAX.☆102Updated last year
- Code Repo for "Subspace Inference for Bayesian Deep Learning"☆83Updated 9 months ago
- Bayes-Newton—A Gaussian process library in JAX, with a unifying view of approximate Bayesian inference as variants of Newton's method.☆232Updated last year
- This is a collection of code samples aimed at illustrating temporal parallelization methods for sequential data.☆31Updated last year
- Code for Randomly Projected Additive Gaussian Processes☆25Updated 5 years ago
- 🤿 Implementation of doubly stochastic deep Gaussian Process using GPflow and TensorFlow 2.0☆27Updated last year
- Robust initialisation of inducing points in sparse variational GP regression models.☆33Updated 2 years ago
- Code repo for "Function-Space Distributions over Kernels"☆31Updated 4 years ago
- PyTorch code of "Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows" (NeurIPS 2020)☆48Updated 4 years ago
- Code for the paper "Bayesian Neural Network Priors Revisited"☆57Updated 3 years ago
- Approximate inference for Markov Gaussian processes using iterated Kalman smoothing, in JAX☆98Updated last year
- Heterogeneous Multi-output Gaussian Processes☆52Updated 4 years ago
- Demos for the paper Generalized Variational Inference (Knoblauch, Jewson & Damoulas, 2019)☆20Updated 5 years ago
- Experiments for Neural Flows paper☆94Updated 3 years ago
- Extensible Tensorflow library for differentiable particle filtering. ICML 2021.☆41Updated 2 years ago
- Reference implementation of variational sequential Monte Carlo proposed by Naesseth et al. "Variational Sequential Monte Carlo" (2018)☆65Updated 5 years ago
- [ICML 2021] Deep Learning for Functional Data Analysis with Adaptive Basis Layers☆25Updated 2 years ago
- Continual Gaussian Processes☆32Updated last year