Implementation of a NER Tagging algorithm with Hidden Markov Model.

🤔 Methods for measuring and visualising the uncertainty in neural networks

Lightweight package for utilizing the Laplace Approximation to compare Bayesian models

Bayesian Analysis in Python (2nd ed.) with Numpyro

Base R Implementation of Logistic Regression from Scratch with Regularization, Laplace Approximation and more

Laplace approximation of the marginal likelihood

Third year mathematics dissertation on variational, laplace and mcmc approximations of bayesian logistic regression

Code accompanying ICLR 2024 paper "Function-space Parameterization of Neural Networks for Sequential Learning"

We provide two notebooks that enable users to explore and experiment with some BDL techniques as Ensembles, MC Dropout and Laplace Approximation. In this way, they allow you to intuitively visualize the main differences among them in a Simulated Dataset and Boston Dataset.

AutoDiff-Inference: Automatic Differentiation Inference. This Repository combines ADVI's change of variable power with Laplace Approximation to provide better inference for constrained parameters. Work done as a part of development of Bijax

Fit and evaluate nonlinear regression models.

Notebooks for Advanced Statistical Inference(ASI) course at EURECOM

PyTorch implementation of Sparse Function-space Representation of Neural Networks

Approximate integrals through second-order Taylor expansions

Fit and compare complex models quickly. Laplace Approximation, Variational Bayes, Importance Sampling.

Discrete Bayesian optimization with LLMs, PEFT finetuning methods, and the Laplace approximation.

Code for Accelerated Linearized Laplace Approximation for Bayesian Deep Learning (ELLA, NeurIPS 22')

Bayesian Low-Rank Adaptation for Large Language Models

Official Code: Estimating Model Uncertainty of Neural Networks in Sparse Information Form, ICML2020.