duke-mlss / Duke-MLWS-2019
Duke Machine Learning Winter School 2019
☆27Updated 5 years ago
Related projects ⓘ
Alternatives and complementary repositories for Duke-MLWS-2019
- Canonical normalizing flows☆10Updated 5 years ago
- Sample code for the NIPS paper "Scalable Variational Inference for Dynamical Systems"☆26Updated 5 years ago
- Libraries for Scientific Computing☆10Updated 5 years ago
- ☆30Updated 2 years ago
- Exercises for the Tutorial on Approximate Bayesian Inference at the Data Science Summer School 2018☆22Updated 6 years ago
- Short Course on Optimization for Machine Learning - Slides and Practical Lab - Pre-doc Summer School on Learning Systems, July 3 to 7, 20…☆18Updated 7 years ago
- A variational method for fast, approximate inference for stochastic differential equations.☆43Updated 6 years ago
- The 2020 Version of the Deep Learning Course☆8Updated 4 years ago
- Talks from Neil Lawrence☆53Updated last year
- ☆14Updated 4 years ago
- Probabilistic Solution of Differential Equations☆14Updated 2 years ago
- Code repository for the generalized Galton board example in the paper "Mining gold from implicit models to improve likelihood-free infere…☆33Updated 4 years ago
- Materials for ORIE 7191: Topics in Optimization for Machine Learning☆42Updated 5 years ago
- Material for 'Mathematics of Deep Learning Workshop' (Invited Talk)☆17Updated last year
- CSCS HPC Summer School 2019☆25Updated 5 years ago
- Codebase for "Clairvoyance: a Unified, End-to-End AutoML Pipeline for Medical Time Series"☆13Updated 4 years ago
- Solving stochastic differential equations and Kolmogorov equations by means of deep learning and Multilevel Monte Carlo simulation☆10Updated 3 years ago
- A tutorial for students that surveys basic ML techniques in ipython notebook format.☆22Updated 5 years ago
- Scalable GP Adapter for Time Series Classification☆13Updated 7 years ago
- PyTorch implementation comparison of old and new method of determining eigenvectors from eigenvalues.☆100Updated 3 years ago
- Source for experiments in the Additive Gaussian process paper, as well as extensions relating to dropout.☆21Updated 10 years ago
- Practice with MCMC methods and dynamics (Langevin, Hamiltonian, etc.)☆43Updated 4 years ago
- Gaussian Process and Uncertainty Quantification Summer School 2018☆31Updated last year
- Duke Natural Language Processing Winter School 2020☆21Updated 4 years ago
- ☆13Updated last year
- Contains legacy code and model examples for the paper "BayesFlow: Learning complex stochastic models with invertible neural networks"☆21Updated 3 years ago
- Codebase for "Demystifying Black-box Models with Symbolic Metamodels", NeurIPS 2019.☆49Updated 5 years ago
- Scalable Log Determinants for Gaussian Process Kernel Learning (https://arxiv.org/abs/1711.03481) (NIPS 2017)☆18Updated 7 years ago