Base R Implementation of Logistic Regression from Scratch with Regularization, Laplace Approximation and more
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Updated
Jan 13, 2020 - R
Base R Implementation of Logistic Regression from Scratch with Regularization, Laplace Approximation and more
🤔 Methods for measuring and visualising the uncertainty in neural networks
Approximate integrals through second-order Taylor expansions
Lightweight package for utilizing the Laplace Approximation to compare Bayesian models
Fit and evaluate nonlinear regression models.
PyTorch implementation of Sparse Function-space Representation of Neural Networks
Code accompanying ICLR 2024 paper "Function-space Parameterization of Neural Networks for Sequential Learning"
Laplace approximation of the marginal likelihood
Implementation of a NER Tagging algorithm with Hidden Markov Model.
Bayesian Analysis in Python (2nd ed.) with Numpyro
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 compare complex models quickly. Laplace Approximation, Variational Bayes, Importance Sampling.
Bayesian Low-Rank Adaptation for Large Language Models
Notebooks for Advanced Statistical Inference(ASI) course at EURECOM
Discrete Bayesian optimization with LLMs, PEFT finetuning methods, and the Laplace approximation.
Third year mathematics dissertation on variational, laplace and mcmc approximations of bayesian logistic regression
Code for Accelerated Linearized Laplace Approximation for Bayesian Deep Learning (ELLA, NeurIPS 22')
Effortless Bayesian Deep Learning through Laplace Approximation for Flux.jl neural networks.
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