A curated list of awesome academic research, books, code of ethics, data sets, institutes, newsletters, principles, podcasts, reports, tools, regulations and standards related to Responsible AI and Human-Centered AI.
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Updated
May 23, 2024
A curated list of awesome academic research, books, code of ethics, data sets, institutes, newsletters, principles, podcasts, reports, tools, regulations and standards related to Responsible AI and Human-Centered AI.
Fit interpretable models. Explain blackbox machine learning.
Implementation of Beyond Neural Scaling beating power laws for deep models and prototype-based models
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
Code associated to the InterpretE research paper
A curated list of awesome responsible machine learning resources.
A PyTorch implementation of constrained optimization and modeling techniques
Model interpretability and understanding for PyTorch
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.
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.
Learning active instances on the border in the case of imbalanced data classification task.
A collection of research materials on explainable AI/ML
NAISR: A 3D Neural Additive Model for Interpretable Shape Representation
SIDU: SImilarity Difference and Uniqueness method for explainable AI
Python library to explain Tree Ensemble models (TE) like XGBoost, using a rule list.
Final Year Project Try-Out Codes
JAX-based Model Explanation and Interpretation Library
Zennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.
AI Division, Reverse Engineering CNN Trojans
ICCV2021 paper: Interpretable Image Recognition by Constructing Transparent Embedding Space (TesNet)
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