Learn fast, scalable, and calibrated measures of uncertainty using neural networks!
-
Updated
Aug 31, 2021 - Python
Learn fast, scalable, and calibrated measures of uncertainty using neural networks!
Deep Neural Networks for Call Of Duty Modern Warfare 2019
🍊 📦 Frequent itemsets and association rules mining for Orange 3.
Code for paper "Factual Confidence of LLMs: on Reliability and Robustness of Current Estimators"
A suite of tests to assess attention faithfulness for explainability
I implemented the reinforcement learning based model Upper Confidence Bound in both Python and R
The official PyTorch implementation for the Guided by Gut: Efficient Test-Time Scaling with Reinforced Intrinsic Confidence
Prediction intervals for trees using conformal intervals. Docs at https://pitci.readthedocs.io/en/latest/
A rule-based classification approach called Associative Classification (AC) normally constructs accurate classifiers from supervised learning data sets in data mining
[ACL 2025] CER: Confidence Enhanced Reasoning in LLMs
Source code for predicting confidence scores for the samples in t-sne embeddings.
Python library for tolerance intervals. Derived from: Derek S. Young (2010). tolerance: An R Package for Estimating Tolerance Intervals. Journal of Statistical Software, 36(5), 1-39. URL http://www.jstatsoft.org/v36/i05/.
Confidence Modeling for Neural Machine Translation.
designing association rule mining model for given data-set using apriori algorithm
Decoding the confidence dataset via SVM, RF, and RNN models.
Working repository for the code of the investigation of minimum soil sample masses
A simple web app for classifying the confidence of speaking by users.
Implementation of Hamming-LUCB algorithm for approximate sorting
Add a description, image, and links to the confidence topic page so that developers can more easily learn about it.
To associate your repository with the confidence topic, visit your repo's landing page and select "manage topics."