A collection of Methods and Models for various architectures of Artificial Neural Networks
-
Updated
Jul 30, 2024 - Python
A collection of Methods and Models for various architectures of Artificial Neural Networks
Repository that contains the projects of the Probabilistic Artificial Intelligence class offered in Fall 2021 at ETH Zurich
Colorectal cancer risk mapping through Bayesian Networks
Exercises for the "Data Analytics" course, University of Bologna (2021/2022)
Control architecture for an agent that perceives, plans, learns, and acts in a 3D MMORPG. Evolved from a monolith through reactive rules, state machines, and utility scoring to GOAP planning. JPS/A* pathfinding, DDA line-of-sight, Bayesian adaptation. Pure Python 3.14 (free-threaded), zero dependencies.
Research code for continual multilingual ASR and code-switching speech recognition with Whisper, Qwen2-Audio, LoRA, Bayesian low-rank factorization, and weight centralization.
Implementation of FOD-learn (fully observed data), Expectation Maximisation(Partially Observed Data) and Latent Variable Learning in Bayesian Network
A theory-driven exploration of meta-learning and generative models for few-shot intelligence using MAML, Reptile, GANs, VAEs, and Diffusion Models.
Reference implementation for AdaptiveStreamFL: Bayesian-enhanced multi-scale federated learning for dynamic data streams.
Python implementation of Naive Bayes Classifier.
Dynamic pricing strategy using Bayesian linear regression and Thompson Sampling in a competitive market environment
Add a description, image, and links to the bayesian-learning topic page so that developers can more easily learn about it.
To associate your repository with the bayesian-learning topic, visit your repo's landing page and select "manage topics."