Fit interpretable models. Explain blackbox machine learning.
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Updated
Jul 28, 2020 - C++
Fit interpretable models. Explain blackbox machine learning.
Blackbox tool to disable SSL certificate validation - including certificate pinning - within iOS and OS X Apps
Open-source implementation of Google Vizier for hyper parameters tuning
An active monitoring software to detect failures before your customers do.
Python Library for Model Interpretation/Explanations
PyTorch implementation of Interpretable Explanations of Black Boxes by Meaningful Perturbation
LinBox - C++ library for exact, high-performance linear algebra
Explain any Black-Box Machine Learning Model with explainX: Fast, Scalable & State-of-the-art Explainable AI Platform.
Towards Reverse-Engineering Black-Box Neural Networks, ICLR'18
Desktop Independent Power Manager
In this part, i've introduced and experimented with ways to interpret and evaluate models in the field of image. (Pytorch)
Multi-core BUGS for fast Bayesian inference of large hierarchical models
model-describer : Making machine learning interpretable to humans
Backtrace-on-throw C++ exception logger
A collection of black-box optimizers with a focus on evolutionary algorithms
Lambdabox provides a generic embedded domain specific language (EDSL) in Haskell for communication with multiple cryptocurrency exchanges.
Tiny Tutorial on https://arxiv.org/abs/1703.04730
Self-Explaining Neural Networks
The project page of paper: Projection & Probability-Driven Black-Box Attack [CVPR 2020]
ZebX Desktop BB Window Manager Interface.
The White Box Project is a project that introduces many ways to solve the part of the black box of machine learning. This project is based on Interpretable Machine Learning by Christoph Molnar. I recommend you to read the book first and practice this project.
Ansible role for Prometheus Blackbox exporter
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