Stars
A LLM-enabled semantic search for neurips abstracts. Including web interface and RAG chatbot.
A toolkit for visualizations in materials informatics.
A Python toolbox to create adversarial examples that fool neural networks in PyTorch, TensorFlow, and JAX
The net:cal calibration framework is a Python 3 library for measuring and mitigating miscalibration of uncertainty estimates, e.g., by a neural network.
Extract entries from a .bib file that are cited in a .tex file
Nvim Treesitter configurations and abstraction layer
Jupyter Notebooks as Markdown Documents, Julia, Python or R scripts
Uncertainty quantification with PyTorch
Open-source framework for uncertainty and deep learning models in PyTorch 🌱
A public repository collecting links to state of the art QA and evaluation sets for various ML and LLM applications
Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)
Website containing illustrations about Machine Learning theory!
Lightning-UQ-Box: Uncertainty Quantification for Neural Networks with PyTorch and Lightning
💻 A better and friendly vi(vim) mode plugin for ZSH.
This repository contains a collection of surveys, datasets, papers, and codes, for predictive uncertainty estimation in deep learning models.
Deploy Dask on job schedulers like PBS, SLURM, and SGE
🎨 A succinct matplotlib wrapper for making beautiful, publication-quality graphics
A scikit-learn-compatible library for estimating prediction intervals and controlling risks, based on conformal predictions.
sbi is a Python package for simulation-based inference, designed to meet the needs of both researchers and practitioners. Whether you need fine-grained control or an easy-to-use interface, sbi has …
🏆 A ranked list of awesome atomistic machine learning projects ⚛️🧬💎.
Notebooks about Bayesian methods for machine learning
High-quality implementations of standard and SOTA methods on a variety of tasks.
Materials Learning Algorithms. A framework for machine learning materials properties from first-principles data.
An implementation of chunked, compressed, N-dimensional arrays for Python.