agramfort / DS3_practical_optim_for_ml
Notebooks from DS3 course on practical optimization
☆15Updated 4 years ago
Alternatives and similar repositories for DS3_practical_optim_for_ml:
Users that are interested in DS3_practical_optim_for_ml are comparing it to the libraries listed below
- Riemannian Convex Potential Maps☆67Updated last year
- Normalizing Flows using JAX☆82Updated last year
- ☆15Updated 4 years ago
- Repository for DTU Special Course, focusing on Variational Inference using Normalizing Flows (VINF). Supervised by Michael Riis Andersen☆25Updated 4 years ago
- Code for "'Hey, that's not an ODE:' Faster ODE Adjoints via Seminorms" (ICML 2021)☆86Updated 2 years ago
- Relative gradient optimization of the Jacobian term in unsupervised deep learning, NeurIPS 2020☆21Updated 3 years ago
- ☆53Updated 6 months ago
- An ultra-lightweight JAX implementation of sparse Gaussian processes via pathwise sampling.☆22Updated 3 years ago
- Approximate inference for Markov Gaussian processes using iterated Kalman smoothing, in JAX☆98Updated last year
- PyTorch implementation of Continuously Indexed Flows paper, with many baseline normalising flows☆31Updated 3 years ago
- ☆80Updated 3 years ago
- Support material for MAT6115, Université de Montréal, Fall 2018☆26Updated last year
- Implicit Deep Adaptive Design (iDAD): Policy-Based Experimental Design without Likelihoods☆18Updated 3 years ago
- Fully Bayesian Inference in GPs - Gaussian and Generic Likelihoods☆22Updated last year
- [NeurIPS'19] Deep Equilibrium Models Jax Implementation☆39Updated 4 years ago
- Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design☆30Updated 3 years ago
- A framework for composing Neural Processes in Julia☆76Updated 3 years ago
- Code repo for "Function-Space Distributions over Kernels"☆31Updated 4 years ago
- Demos for the paper Generalized Variational Inference (Knoblauch, Jewson & Damoulas, 2019)☆20Updated 5 years ago
- RKHS feature vectors, operators, and statistical models using JAX for automatic differentiation☆8Updated 3 years ago
- A differentiation API for PyTorch☆30Updated 4 years ago
- Tutorial materials of the Probabilistic Numerics Spring School.☆34Updated last year
- Riemannian Optimization Using JAX☆48Updated last year
- ☆22Updated 4 years ago
- PyTorch linear operators for curvature matrices (Hessian, Fisher/GGN, KFAC, ...)☆31Updated this week
- A library implementing the kernels for and experiments using extrinsic gauge equivariant vector field Gaussian Processes☆25Updated 3 years ago
- Code to accompany paper 'Bayesian Deep Ensembles via the Neural Tangent Kernel'☆27Updated 3 years ago
- Probabilistic Solution of Differential Equations☆13Updated 2 years ago
- ☆30Updated 5 months ago
- Neural likelihood-free methods in PyTorch.☆39Updated 5 years ago