This project aim to understad if a deep learning model is calibrated (average accuracy match average confident) using Reliability Diagram and perform a re-calibration by the training with Focal Loss.
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Updated
Feb 21, 2023 - Jupyter Notebook
This project aim to understad if a deep learning model is calibrated (average accuracy match average confident) using Reliability Diagram and perform a re-calibration by the training with Focal Loss.
Code for "Conformal Performance Range Prediction for Segmentation Output Quality Control" accepted to MICCAI UNSURE 2024
An Uncertainty-Aware Pseudo-Label Selection Framework using Regularized Conformal Prediction
Code for my Bachelor's thesis "Drone image and video encoding and processing using machine learning"
Conformal Off-policy Prediction
A graphical user interface (GUI) and web application to facilitate the usage of ENS-Score.
A serverless function is utilized to evaluate the divergence of a particular output from the established log-likelihood set by a language model. This function is designed to compute the log-likelihood per message. Subsequently, p-values are generated and used as a prediction interval to categorize, appropriately append, and sort LLM output
This is a small demo of Conformal Prediction given to Prof. Glenn Shafer's class in Feb-2018. Compares Conformal Prediction to plain Linear Regression using the Boston dataset from R
Conformal Predictions using photonai
Conformal Prediction + Federated Learning
Code for "Controlling Counterfactual Harm in Decision Support Systems Based on Prediction Sets", Arxiv 2024.
Clusters protein chains based on CA distance difference
Machine Learning Assignment
use cnn and conformal prediction to deal with mutilabel image issues
This repository contains jupyter notebooks providing an implementation of basic Machine Learning models for regression and classification.
Conformal Bayesian Computation (CBC). This paper summarizes the theoretical foundings of the CBC, as well as it applies to 2 use-cases: classification and regression.
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