This project provides tools to simulate access strategies and cache-content advertisement schemes for distributed caching.
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Updated
Jun 1, 2024 - ReScript
This project provides tools to simulate access strategies and cache-content advertisement schemes for distributed caching.
Implemented the FedAvg Algorithm in Federated Learning. Completed its uncertainty estimation and confidence calibration.
Uncertainty Toolbox: a Python toolbox for predictive uncertainty quantification, calibration, metrics, and visualization
Awesome-LLM-Robustness: a curated list of Uncertainty, Reliability and Robustness in Large Language Models
A professionally curated list of awesome Conformal Prediction videos, tutorials, books, papers, PhD and MSc theses, articles and open-source libraries.
This repository contains a collection of surveys, datasets, papers, and codes, for predictive uncertainty estimation in deep learning models.
[CVPR 2024 Oral] Producing and Leveraging Online Map Uncertainty in Trajectory Prediction
(CVPR 2024) Pytorch implementation of “SURE: SUrvey REcipes for building reliable and robust deep networks”
Quantile Regression Forests compatible with scikit-learn.
[CVPR 2024 Oral - Best paper award candidate] Official repository of "PaSCo: Urban 3D Panoptic Scene Completion with Uncertainty Awareness"
Conformal anomaly detection wrapper for uncertainty-quantified 'PyOD' anomaly detectors.
A Sensitivity and uncertainty analysis toolbox for Python based on the generalized polynomial chaos method
Official code for "ZigZag: Universal Sampling-free Uncertainty Estimation Through Two-Step Inference" (TMLR 2024)
Implementation of the paper "Hubble Meets Webb: Image-to-Image Translation in Astronomy", published in MDPI Sensors
An extension of LightGBM to probabilistic modelling
Implements methods to train semantic segmentation networks to perform high-quality uncertainty estimation on distributionally-shifted images.
Affine Invariant Markov Chain Monte Carlo (MCMC) Ensemble sampler
Mathematica notebook for h-statistics based MC and MLMC covariance estimation
Counterfactuals: Take the uncertainty out of your machine learning models
We derive a fundamental property of the posterior distribution in Gaussian denoising, and use it to propose a new way for uncertainty visualization, which requires no training or fine-tuning.
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