Teodor Chiaburu's Projects
Algorithms for explaining machine learning models
Image augmentation library in Python for machine learning.
XAI Experiments on an Annotated Dataset of Wild Bee Images
Repository for the explanation method Calibrated Explanations (CE)
Concept Bottleneck Models, ICML 2020
Causal Explanation (CXPlain) is a method for explaining the predictions of any machine-learning model.
The official repository for Deformable ProtoPNet, as described in "Deformable ProtoPNet: An Interpretable Image Classifier Using Deformable Prototypes".
3 Step tutorial how to use docker
Implementation of the Geometric SMOTE over-sampling algorithm.
A list of commonly used Git commands
Building a ResNet50-Classifier in TensorFlow-Keras to tackle Google's classification challenge
π Influenciae is a Tensorflow Toolbox for Influence Functions
A toolbox to iNNvestigate neural networks' predictions!
Code repository for "Interpretable and Accurate Fine-grained Recognition via Region Grouping", CVPR 2020 (Oral)
SAM with text prompt
This is a PyTorch implementation of the paper "Multi-branch and Multi-scale Attention Learning for Fine-Grained Visual Categorization (MMAL-Net)" (Fan Zhang, Meng Li, Guisheng Zhai, Yizhao Liu).
Local explanations with uncertainty π!
NLX-GPT: A Model for Natural Language Explanations in Vision and Vision-Language Tasks, CVPR 2022 (Oral)
Bike sharing dataset analyzed with Multilinear Regression, Random Forests and Time Series
This code package implements the prototypical part network (ProtoPNet) from the paper "This Looks Like That: Deep Learning for Interpretable Image Recognition" (to appear at NeurIPS 2019), by Chaofan Chen* (Duke University), Oscar Li* (Duke University), Chaofan Tao (Duke University), Alina Jade Barnett (Duke University), Jonathan Su (MIT Lincoln Laboratory), and Cynthia Rudin (Duke University) (* denotes equal contribution).
Code for "Interpretable image classification with differentiable prototypes assignment", ECCV 2022
ProtoTrees: Neural Prototype Trees for Interpretable Fine-grained Image Recognition, published at CVPR2021
Time series forecasting with PyTorch
Quantus is an eXplainable AI toolkit for responsible evaluation of neural network explanations
The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
An app to aproximate important functions using the Taylor series
Merged repositories for TCAV and ACE
TEASER - Tool for Energy Analysis and Simulation for Efficient Retrofit