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Official Repository of the Entity-based Reinforcement Learning for Autonomous Cyber Defence paper.
A Python library for Secure and Explainable Machine Learning
Adaptation of PrimAITE for the AEGIS project - Enhancing Situational Awareness of Language-based Blue Agents through Graph Neural Prompting
A software project to provide the target tracking community with a framework for the development and testing of tracking algorithms.
Source code about machine learning and security.
Machine Learning Engineering Open Book
Zero-copy MPI communication of JAX arrays, for turbo-charged HPC applications in Python ⚡
Galactic and Gravitational Dynamics in Python (+ GPU and autodiff)
ML/Stats based predictions to UEFA Euro 2024
Open-source Python implementations of Efficient Hypothesis Management (EHM) algorithms.
Multimodal contrastive pretraining for astronomical data
R package for poll retrieval from pollofpolls.eu and poll aggregation (trends) calculations
🔥Highlighting the top ML papers every week.
A curated list of resources dedicated to reinforcement learning applied to cyber security.
The TTCP CAGE Challenges are a series of public challenges instigated to foster the development of autonomous cyber defensive agents. This CAGE Challenge 4 (CC4) returns to a defence industry enter…
Official reinforcement learning environment for demand response and load shaping
JAX-SPH: A Differentiable Smoothed Particle Hydrodynamics Framework
Official implementation of HARL algorithms based on PyTorch.
A comprehensive toolbox for executing Sequential Monte Carlo (SMC) methods.
A python implementation of NUTS working in an SMC sampler using bridge-Stan to define and execute models
Python package for Causal Discovery by learning the graphical structure of Bayesian networks. Structure Learning, Parameter Learning, Inferences, Sampling methods.
scida is an out-of-the-box analysis tool for large scientific datasets. It primarily supports the astrophysics community, focusing on cosmological and galaxy formation simulations using particles o…
Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).