ImperialCollegeLondon / RCDS-intro-to-containers
☆27Updated 2 months ago
Alternatives and similar repositories for RCDS-intro-to-containers:
Users that are interested in RCDS-intro-to-containers are comparing it to the libraries listed below
- ☆32Updated 4 months ago
- Materials of the Nordic Probabilistic AI School 2024.☆62Updated 8 months ago
- Official LaTeX templates employing the Imperial College London brand.☆26Updated 2 months ago
- Distance-based Analysis of DAta-manifolds in python☆122Updated last week
- Library for diffusion maps☆46Updated 3 years ago
- ☆22Updated 7 months ago
- ☆14Updated 3 months ago
- Python Tensor Toolbox☆31Updated last week
- Fully and Partially Bayesian Neural Nets☆66Updated 2 weeks ago
- Gaussian Processes for Experimental Sciences☆221Updated 5 months ago
- Invariant representation learning from imaging and spectral data☆50Updated last year
- ☆11Updated last year
- Simulation-based inference benchmark☆97Updated 2 months ago
- The sweet source coding library (Python and C++)☆11Updated 3 months ago
- Cryo electron microscopy image simulation and analysis built on JAX.☆33Updated this week
- ☆127Updated 6 years ago
- Sparse Linear Regression Models☆17Updated this week
- Boltzmann Generators and Normalizing Flows in PyTorch☆160Updated last year
- PyBADS: Bayesian Adaptive Direct Search optimization algorithm for model fitting in Python☆72Updated 2 months ago
- Python implementation of the force and diffusion inference method described in (Frishman and Ronceray, Phys. Rev. X 10, 021009, 2020).☆28Updated 2 years ago
- SBI Workshop jointly by Helmholtz AI + ML ⇌ Science Colaboratory☆23Updated last year
- Python energy landscape explorer☆98Updated 5 years ago
- A Python module for fast computation of 2D and 3D radial distribution functions (RDFs).☆28Updated last year
- Community-sourced list of papers and resources on neural simulation-based inference.☆113Updated 2 months ago
- Regression datasets from the UCI repository with standardized test-train splits.☆44Updated 2 years ago
- Notebooks for "A high bias low-variance introduction to Machine Learning for physicists."☆69Updated 5 years ago
- Tutorials for the book.☆12Updated 3 years ago
- ☆88Updated 3 years ago
- Spring 2023 seminar on automated experiment☆23Updated last year
- Set of Lecture at Duke in 2018 by Lenka Zdeborova and Florent Krzakala "Statistical Physics For Optimization and Learning"☆16Updated 5 years ago