Official Code for "Confidence Matters: Enhancing Medical Image Classification Through Uncertainty-Driven Contrastive Self-distillation" accepted at MICCAI2024
A repo for toy examples to test uncertainties estimation of neural networks
Uncertainty quantification fo ML - collection of scripts, tutorials and templates
Simple and efficient way of performing deep ensembling to improve robustness as well as estimate uncertainty
Code for "Deal: Deep Evidential Active Learning for Image Classification" (ICMLA 2020)
[MIPR 2024 Invited] Code for the paper: Counterfactual Gradients-based Quantification of Prediction Trust in Neural Networks
Probabilistic framework for solving Visual Dialog
Introducing a novel lightweight, post-hoc, single-pass, model-agnostic uncertainty quantification model for pretrained deep neural networks, designed for efficiency, scalability, and compatibility.
Official repository for the paper "Masksembles for Uncertainty Estimation" (CVPR2021).
Wasserstein dropout (W-dropout) is a novel technique to quantify uncertainty in regression networks. It is fully non-parametric and yields accurate uncertainty estimates - even under data shifts.
This repository contains code and resources for my thesis project on uncertainty estimation in computed tomography (CT) scan modeling. Explore Bayesian and deterministic neural network architectures for CT analysis and compare their effectiveness in quantifying uncertainty.
A repository about Robust Deep Neural Networks with Uncertainty, Local Competition and Error-Correcting-Output-Codes in TensorFlow.
Detecting Negation and Uncertainty using various methods
Guided Perturbations: Self-Corrective Behavior in Convolutional Neural Networks
A neural-network based image classifier that quantifies its uncertainty using Bayesian methods, as described in Kendall and Gal (2017)
Behaviour Cloning of Cartpole Swing-up Policy with Model-Predictive Uncertainty Regularization (UW CSE571 Guided Project)
Official Code: Trust Your Robots! Predictive Uncertainty Estimation of Neural Networks with Sparse Gaussian Processes
📊 Explore Bayesian statistics and econometrics with training materials designed for quantitative analysts and grad students in machine learning.
🛠️ Extend native JavaScript prototypes with the extend-core library, adding useful utility methods for Arrays, Dates, Strings, and more in TypeScript.
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