99 Open source projects that are alternatives of or similar to kernel-mod

Awesome Resources for Advanced Computer Vision Topics

Text classification models. Used a submodule for other projects.

Class activation maps for your PyTorch models (CAM, Grad-CAM, Grad-CAM++, Smooth Grad-CAM++, Score-CAM, SS-CAM, IS-CAM, XGrad-CAM)

A curated list of awesome Fairness in AI resources

A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convolutional Network.

Hierarchical Bayesian estimation and hypothesis testing for delay discounting tasks

🏥 Visualizing Convolutional Networks for MRI-based Diagnosis of Alzheimer’s Disease

Python implementation of supervised PCA, supervised random projections, and their kernel counterparts.

Algorithms for monitoring and explaining machine learning models

A collection of research materials on explainable AI/ML

Layer-wise Relevance Propagation (LRP) for LSTMs

Interesting resources related to XAI (Explainable Artificial Intelligence)

Hypothesis and statistical testing in Python

⬛ Python Individual Conditional Expectation Plot Toolbox

Kernel k Nearest Neighbors in R

Causal Explanation (CXPlain) is a method for explaining the predictions of any machine-learning model.

Misc Statistics and Machine Learning codes in R

Automatic equation building and curve fitting. Runs on Tensorflow. Built for academia and research.

distfit is a python library for probability density fitting.

Contrastive Explanation (Foil Trees), developed at TNO/Utrecht University

Model interpretability and understanding for PyTorch

moDel Agnostic Language for Exploration and eXplanation

A python package for graph kernels, graph edit distances, and graph pre-image problem.

XAI - An eXplainability toolbox for machine learning

A Python package implementing a new machine learning model for text classification with visualization tools for Explainable AI

📍 Interactive Studio for Explanatory Model Analysis

Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.

No description or website provided.

StellarGraph - Machine Learning on Graphs

A Matlab benchmarking toolbox for kernel adaptive filtering

Pytorch Implementation of recent visual attribution methods for model interpretability

Bioinformatics library in Kotlin

Model Agnostics breakDown plots

Collection of NLP model explanations and accompanying analysis tools

Code repository for "Interpretable and Accurate Fine-grained Recognition via Region Grouping", CVPR 2020 (Oral)

Enabling easy statistical significance testing for deep neural networks.

A collection of tools for reverse engineering neural networks.

FRP: Fast Random Projections

A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning

Python package for multivariate hypothesis testing

💡 A curated list of adversarial attacks on model explanations

A Julia package for generating timeseries surrogates

General Interpretability Package

💭 Aspect-Based-Sentiment-Analysis: Transformer & Explainable ML (TensorFlow)

Codebase for "Demystifying Black-box Models with Symbolic Metamodels", NeurIPS 2019.

UAI 2015. Kernel-based just-in-time learning for expectation propagation

[ICCV 2017] Torch code for Grad-CAM

Explainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code.

Interpretability Methods for tf.keras models with Tensorflow 2.x

A collection of important graph embedding, classification and representation learning papers with implementations.

Federated Learning Library: https://fedml.ai

Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).

Visualization toolkit for neural networks in PyTorch! Demo -->

NeurIPS 2016. Linear-time interpretable nonparametric two-sample test.

A curated list of awesome machine learning interpretability resources.

Machine learning for beginner(Data Science enthusiast)

Adaptive, interpretable wavelets across domains (NeurIPS 2021)

This is the page for the book Digital Signal Processing with Kernel Methods.

Methods for computational information geometry

A game theoretic approach to explain the output of any machine learning model.