Webapp/Application implemention of my thesis about XAI and Interpretability of Transformer Models.
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Updated
Feb 14, 2024 - Python
Webapp/Application implemention of my thesis about XAI and Interpretability of Transformer Models.
Understanding Morphosyntactic Representations in Pretrained Language Models.
ICCV2021 paper: Interpretable Image Recognition by Constructing Transparent Embedding Space (TesNet)
A python project for prototype-based soft feature selection
B.Tech Project
Neural Additive Models - Visualization Tool in PyTorch/Plotly-Dash
IN PROGRESS - after the paper "Shapley-Lorenz decompositions in eXplainable Artificial Intelligence" by Giudici and Raffinetti - 2020
Prototype based ML implementation for Multiple reject thresholds for improving classification reliability
An end-to-end implementation of Breast Cancer Detection using prosemble ML package within the Flask framework integrated in PyWebIO with deployment on Heroku platform as a service cloud.
End-to-end implementation of banknotes authentication using advanced prototype-based model classification by components model dockerized and deployed on the Heroku platform as a service cloud.
An end-to-end implementation of Breast Cancer Detection using prosemble ML package within the fastapi framework with deployment on Heroku platform as a service cloud.
Repository of the paper "Knowledge-Grounded Target Group Language Recognition in Hate Speech" (SEMANTiCS 2023).
An end-to-end implementation of Breast Cancer Detection using prosemble ML package within the Flask framework and Flasgger dockerized for deployment
The official implementation of CROMP (Constrained Regression with Ordered and Margin-sensitive Parameters) along with experimental test pipeline
A python project for prototype-based feature selection
Code for the paper Mutation Validation for Learning Vector Quantization.
Prototype-based Feature selection with the Nafes Package
Prototype-Based Soft Feature Selection Package
Learning active instances on the border in the case of imbalanced data classification task.
Explain fully connected ReLU neural networks using rules
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