MI^2 DataLab (MI2DataLab)

MI^2 DataLab

MI2DataLab

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Location:Warsaw, Poland

Home Page:https://mi2.ai/

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MI^2 DataLab's repositories

survshap

SurvSHAP(t): Time-dependent explanations of machine learning survival models

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pyBreakDown

Python implementation of R package breakDown

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MI2DataLab_Seminarium

Materiały z seminariów prowadzonych w MI^2 DataLabie.

memr

R package for Multisource Embeddings for Medical Records

Language:RLicense:NOASSERTIONStargazers:15Issues:10Issues:6

ResponsibleML-UseR2022

Materials for ,,Introduction to Responsible ML at UseR!2022''

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survxai

Explanations for survival models

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HADES

A powerful tool for comparing similarly structured documents

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xlungs-trustworthy-los-prediction

Trustworthy LoS Prediction Based on Multi-modal Data (AIME 2023)

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fooling-partial-dependence

Fooling Partial Dependence via Data Poisoning (ECML PKDD 2022)

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MI2-prace-dyplomowe

Find out more at: http://mi2.mini.pw.edu.pl/

LIMEcraft

LIMEcraft: Handcrafted superpixel selectionand inspection for Visual eXplanations

Language:Jupyter NotebookLicense:GPL-3.0Stargazers:5Issues:5Issues:0

rashomon-quartet

Performance is not enough: the story of Rashomon's quartet

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crs19-monitor

Monitor of SARS-CoV-2 variants and mutations

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MI2-DobreRady

Zbiór rzeczy o których warto pamiętać przygotowując pracę dyplomową lub przygotowując się do obrony

SeFNet-Healthcare

This repository contains code and resources that can be used to reproduce the results presented in the article "SeFNet: Bridging Tabular Datasets with Semantic Feature Nets"

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tidycharts

https://mi2datalab.github.io/tidycharts/

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call-for-collaborators

Summer projects in the MI2 DataLab

rai-covid

Responsible Prediction Making of COVID-19 Mortality (AAAI 2021)

License:NOASSERTIONStargazers:3Issues:10Issues:0

autoexplainer

Automated explanation selection for convolutional neural networks

Language:Jupyter NotebookLicense:MITStargazers:1Issues:3Issues:0
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Rashomon-detect

Rashomon-detect algorithm

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survival-xai-tutorial

Materials for tutorial: Dancing with censored data: how to survive with explainable survival analysis? at MLinPL 2023

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be-careful-evaluating-explanations

Be careful when evaluating explanations regarding ground truth

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DeCoviD

Detection of Covid-19 related markers of pulmonary changes using Deep Neural Networks models supported by eXplainable Artificial Intelligence and Cognitive Compressed Sensing

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iema

The Grammar of Interactive Explanatory Model Analysis

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xlungs_www

Website of xlungs project

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