An open API service providing repository metadata for many open source software ecosystems.

Topic: "explainability"

shap/shap

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

Language: Jupyter Notebook - Size: 282 MB - Last synced at: 7 days ago - Pushed at: 8 days ago - Stars: 23,949 - Forks: 3,369

EthicalML/awesome-production-machine-learning

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

Size: 2.43 MB - Last synced at: 16 days ago - Pushed at: 16 days ago - Stars: 18,486 - Forks: 2,351

interpretml/interpret

Fit interpretable models. Explain blackbox machine learning.

Language: C++ - Size: 14.8 MB - Last synced at: 6 days ago - Pushed at: 6 days ago - Stars: 6,516 - Forks: 749

MAIF/shapash

🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

Language: Jupyter Notebook - Size: 61.8 MB - Last synced at: 22 days ago - Pushed at: 25 days ago - Stars: 2,881 - Forks: 346

hila-chefer/Transformer-Explainability

[CVPR 2021] Official PyTorch implementation for Transformer Interpretability Beyond Attention Visualization, a novel method to visualize classifications by Transformer based networks.

Language: Jupyter Notebook - Size: 3.76 MB - Last synced at: 19 days ago - Pushed at: over 1 year ago - Stars: 1,886 - Forks: 250

microsoft/responsible-ai-toolbox

Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.

Language: TypeScript - Size: 111 MB - Last synced at: 6 days ago - Pushed at: 4 months ago - Stars: 1,552 - Forks: 409

EthicalML/xai

XAI - An eXplainability toolbox for machine learning

Language: Python - Size: 17.8 MB - Last synced at: 19 days ago - Pushed at: over 3 years ago - Stars: 1,171 - Forks: 179

hila-chefer/Transformer-MM-Explainability

[ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.

Language: Jupyter Notebook - Size: 25.3 MB - Last synced at: 18 days ago - Pushed at: almost 2 years ago - Stars: 851 - Forks: 110

h1st-ai/h1st

Power Tools for AI Engineers With Deadlines

Language: Jupyter Notebook - Size: 15.2 MB - Last synced at: 30 days ago - Pushed at: almost 2 years ago - Stars: 793 - Forks: 92

MisaOgura/flashtorch

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

Language: HTML - Size: 68.8 MB - Last synced at: 22 days ago - Pushed at: over 1 year ago - Stars: 740 - Forks: 87

flyingdoog/awesome-graph-explainability-papers

Papers about explainability of GNNs

Size: 398 KB - Last synced at: about 1 month ago - Pushed at: 3 months ago - Stars: 719 - Forks: 68

alvinwan/neural-backed-decision-trees

Making decision trees competitive with neural networks on CIFAR10, CIFAR100, TinyImagenet200, Imagenet

Language: Python - Size: 2.57 MB - Last synced at: 18 days ago - Pushed at: about 2 years ago - Stars: 621 - Forks: 131

mmschlk/shapiq

Shapley Interactions and Shapley Values for Machine Learning

Language: Python - Size: 309 MB - Last synced at: 1 day ago - Pushed at: 1 day ago - Stars: 557 - Forks: 35

explainX/explainx

Explainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code. We are looking for co-authors to take this project forward. Reach out @ [email protected]

Language: Jupyter Notebook - Size: 61.3 MB - Last synced at: 18 days ago - Pushed at: 10 months ago - Stars: 436 - Forks: 56

haofanwang/Score-CAM

Official implementation of Score-CAM in PyTorch

Language: Python - Size: 2.22 MB - Last synced at: 16 days ago - Pushed at: almost 3 years ago - Stars: 423 - Forks: 65

xmed-lab/CLIP_Surgery

CLIP Surgery for Better Explainability with Enhancement in Open-Vocabulary Tasks

Language: Jupyter Notebook - Size: 18.9 MB - Last synced at: 3 months ago - Pushed at: 3 months ago - Stars: 397 - Forks: 25

wisent-ai/wisent-guard

This is an open-source version of the representation engineering framework for stopping harmful outputs or hallucinations on the level of activations. 100% free, self-hosted and open-source.

Language: Python - Size: 53.9 MB - Last synced at: 6 days ago - Pushed at: 7 days ago - Stars: 335 - Forks: 19

keisen/tf-keras-vis

Neural network visualization toolkit for tf.keras

Language: Python - Size: 97.1 MB - Last synced at: 13 days ago - Pushed at: 3 months ago - Stars: 325 - Forks: 45

hbaniecki/adversarial-explainable-ai

💡 Adversarial attacks on explanations and how to defend them

Size: 2.62 MB - Last synced at: 3 months ago - Pushed at: 6 months ago - Stars: 314 - Forks: 48

carla-recourse/CARLA

CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms

Language: Python - Size: 1.89 MB - Last synced at: 17 days ago - Pushed at: over 1 year ago - Stars: 288 - Forks: 64

iancovert/sage

For calculating global feature importance using Shapley values.

Language: Python - Size: 7.93 MB - Last synced at: 1 day ago - Pushed at: 8 days ago - Stars: 271 - Forks: 35

sebastian-hofstaetter/matchmaker

Training & evaluation library for text-based neural re-ranking and dense retrieval models built with PyTorch

Language: Python - Size: 10 MB - Last synced at: 4 months ago - Pushed at: over 2 years ago - Stars: 261 - Forks: 29

dmlc/GNNLens2

Visualization tool for Graph Neural Networks

Language: TypeScript - Size: 20.9 MB - Last synced at: 16 days ago - Pushed at: over 2 years ago - Stars: 251 - Forks: 27

AI4LIFE-GROUP/OpenXAI

OpenXAI : Towards a Transparent Evaluation of Model Explanations

Language: JavaScript - Size: 41.7 MB - Last synced at: about 1 month ago - Pushed at: 10 months ago - Stars: 245 - Forks: 43

chr5tphr/zennit

Zennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.

Language: Python - Size: 2.28 MB - Last synced at: 17 days ago - Pushed at: 11 months ago - Stars: 226 - Forks: 34

squaredev-io/whitebox

[Not Actively Maintained] Whitebox is an open source E2E ML monitoring platform with edge capabilities that plays nicely with kubernetes

Language: Python - Size: 21.2 MB - Last synced at: about 1 month ago - Pushed at: almost 2 years ago - Stars: 183 - Forks: 5

feedzai/timeshap

TimeSHAP explains Recurrent Neural Network predictions.

Language: Jupyter Notebook - Size: 1.53 MB - Last synced at: 16 days ago - Pushed at: over 1 year ago - Stars: 175 - Forks: 32

hate-alert/HateXplain

Can we use explanations to improve hate speech models? Our paper accepted at AAAI 2021 tries to explore that question.

Language: Python - Size: 6.57 MB - Last synced at: about 1 year ago - Pushed at: almost 2 years ago - Stars: 173 - Forks: 62

charlesdedampierre/BunkaTopics

🗺️ Data Cleaning and Textual Data Visualization 🗺️

Language: Python - Size: 229 MB - Last synced at: 26 days ago - Pushed at: 26 days ago - Stars: 172 - Forks: 15

mims-harvard/GraphXAI

GraphXAI: Resource to support the development and evaluation of GNN explainers

Language: Python - Size: 249 MB - Last synced at: 8 months ago - Pushed at: about 1 year ago - Stars: 166 - Forks: 29

csinva/imodelsX

Interpret text data using LLMs (scikit-learn compatible).

Language: Python - Size: 35 MB - Last synced at: 6 minutes ago - Pushed at: about 1 hour ago - Stars: 165 - Forks: 26

pietrobarbiero/pytorch_explain

PyTorch Explain: Interpretable Deep Learning in Python.

Language: Jupyter Notebook - Size: 42.1 MB - Last synced at: 23 days ago - Pushed at: about 1 year ago - Stars: 154 - Forks: 14

AstraZeneca/awesome-shapley-value

Reading list for "The Shapley Value in Machine Learning" (JCAI 2022)

Size: 622 KB - Last synced at: about 1 month ago - Pushed at: almost 3 years ago - Stars: 150 - Forks: 12

DFKI-NLP/thermostat

Collection of NLP model explanations and accompanying analysis tools

Language: Jsonnet - Size: 1.37 MB - Last synced at: 20 days ago - Pushed at: almost 2 years ago - Stars: 145 - Forks: 8

all-things-vits/code-samples

Holds code for our CVPR'23 tutorial: All Things ViTs: Understanding and Interpreting Attention in Vision.

Language: Jupyter Notebook - Size: 14.7 MB - Last synced at: over 1 year ago - Pushed at: almost 2 years ago - Stars: 145 - Forks: 9

marakeby/pnet_prostate_paper

P-NET, Biologically informed deep neural network for prostate cancer classification and discovery

Language: HTML - Size: 5.26 MB - Last synced at: over 1 year ago - Pushed at: over 3 years ago - Stars: 137 - Forks: 50

pkuserc/ChatGPT_for_IE

Evaluating ChatGPT’s Information Extraction Capabilities: An Assessment of Performance, Explainability, Calibration, and Faithfulness

Language: Python - Size: 5.78 MB - Last synced at: 10 months ago - Pushed at: 10 months ago - Stars: 136 - Forks: 7

JHoelli/Awesome-Time-Series-Explainability

A list of (post-hoc) XAI for time series

Size: 424 KB - Last synced at: 27 days ago - Pushed at: 9 months ago - Stars: 135 - Forks: 16

csinva/hierarchical-dnn-interpretations

Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)

Language: Jupyter Notebook - Size: 48.7 MB - Last synced at: 28 days ago - Pushed at: almost 4 years ago - Stars: 128 - Forks: 23

hila-chefer/RobustViT

[NeurIPS 2022] Official PyTorch implementation of Optimizing Relevance Maps of Vision Transformers Improves Robustness. This code allows to finetune the explainability maps of Vision Transformers to enhance robustness.

Language: Jupyter Notebook - Size: 16.6 MB - Last synced at: about 1 month ago - Pushed at: over 2 years ago - Stars: 127 - Forks: 13

laura-rieger/deep-explanation-penalization

Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" https://arxiv.org/abs/1909.13584

Language: Jupyter Notebook - Size: 248 MB - Last synced at: about 1 month ago - Pushed at: about 4 years ago - Stars: 127 - Forks: 14

baldassarreFe/graph-network-explainability

Explainability techniques for Graph Networks, applied to a synthetic dataset and an organic chemistry task. Code for the workshop paper "Explainability Techniques for Graph Convolutional Networks" (ICML19)

Language: Jupyter Notebook - Size: 6.48 MB - Last synced at: 2 months ago - Pushed at: over 5 years ago - Stars: 122 - Forks: 16

dylan-slack/TalkToModel

TalkToModel gives anyone with the powers of XAI through natural language conversations 💬!

Language: Python - Size: 10.1 MB - Last synced at: about 2 months ago - Pushed at: almost 2 years ago - Stars: 120 - Forks: 25

d909b/cxplain

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

Language: Python - Size: 262 KB - Last synced at: over 1 year ago - Pushed at: almost 5 years ago - Stars: 118 - Forks: 34

MolecularAI/QSARtuna

QSARtuna: QSAR model building with the optuna framework

Language: Jupyter Notebook - Size: 106 MB - Last synced at: 3 months ago - Pushed at: 8 months ago - Stars: 115 - Forks: 17

givasile/effector

Effector - a Python package for global and regional effect methods

Language: Jupyter Notebook - Size: 125 MB - Last synced at: 6 days ago - Pushed at: 6 days ago - Stars: 111 - Forks: 2

M-Nauta/ProtoTree

ProtoTrees: Neural Prototype Trees for Interpretable Fine-grained Image Recognition, published at CVPR2021

Language: Python - Size: 870 KB - Last synced at: about 1 month ago - Pushed at: almost 3 years ago - Stars: 101 - Forks: 21

salimamoukou/acv00

ACV is a python library that provides explanations for any machine learning model or data. It gives local rule-based explanations for any model or data and different Shapley Values for tree-based models.

Language: Jupyter Notebook - Size: 19 MB - Last synced at: 27 days ago - Pushed at: almost 3 years ago - Stars: 100 - Forks: 11

awslabs/sagemaker-explaining-credit-decisions

Amazon SageMaker Solution for explaining credit decisions.

Language: Python - Size: 2.77 MB - Last synced at: 21 days ago - Pushed at: about 2 years ago - Stars: 97 - Forks: 28

ModelOriented/treeshap

Compute SHAP values for your tree-based models using the TreeSHAP algorithm

Language: R - Size: 19.7 MB - Last synced at: 8 days ago - Pushed at: 11 months ago - Stars: 88 - Forks: 24

SAP-archive/contextual-ai 📦

Contextual AI adds explainability to different stages of machine learning pipelines - data, training, and inference - thereby addressing the trust gap between such ML systems and their users. It does not refer to a specific algorithm or ML method — instead, it takes a human-centric view and approach to AI.

Language: Jupyter Notebook - Size: 53 MB - Last synced at: 11 days ago - Pushed at: almost 2 years ago - Stars: 87 - Forks: 12

snehankekre/streamlit-shap

streamlit-shap provides a wrapper to display SHAP plots in Streamlit.

Language: Python - Size: 4.52 MB - Last synced at: 27 days ago - Pushed at: almost 3 years ago - Stars: 86 - Forks: 9

12wang3/rrl

The code of NeurIPS 2021 paper "Scalable Rule-Based Representation Learning for Interpretable Classification" and TPAMI paper "Learning Interpretable Rules for Scalable Data Representation and Classification"

Language: Python - Size: 561 KB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 84 - Forks: 22

mertyg/post-hoc-cbm

Code for the paper "Post-hoc Concept Bottleneck Models". Spotlight @ ICLR 2023

Language: Python - Size: 1.4 MB - Last synced at: 26 days ago - Pushed at: about 1 year ago - Stars: 77 - Forks: 13

fat-forensics/fat-forensics

Modular Python Toolbox for Fairness, Accountability and Transparency Forensics

Language: Python - Size: 1.95 MB - Last synced at: 9 days ago - Pushed at: about 2 years ago - Stars: 77 - Forks: 14

Yu-Group/adaptive-wavelets

Adaptive, interpretable wavelets across domains (NeurIPS 2021)

Language: Jupyter Notebook - Size: 268 MB - Last synced at: 30 days ago - Pushed at: over 3 years ago - Stars: 77 - Forks: 11

hila-chefer/Conceptor

Official implementation of the paper The Hidden Language of Diffusion Models

Size: 131 MB - Last synced at: about 2 months ago - Pushed at: over 1 year ago - Stars: 72 - Forks: 0

ServiceNow/azimuth

Helping AI practitioners better understand their datasets and models in text classification. From ServiceNow.

Language: Python - Size: 44 MB - Last synced at: 2 months ago - Pushed at: 6 months ago - Stars: 68 - Forks: 7

iancovert/shapley-regression

For calculating Shapley values via linear regression.

Language: Python - Size: 707 KB - Last synced at: 12 days ago - Pushed at: about 4 years ago - Stars: 68 - Forks: 13

flipz357/S3BERT

Semantically Structured Sentence Embeddings

Language: Python - Size: 72.3 KB - Last synced at: 8 months ago - Pushed at: 8 months ago - Stars: 65 - Forks: 5

deel-ai/influenciae

👋 Influenciae is a Tensorflow Toolbox for Influence Functions

Language: Python - Size: 2.03 MB - Last synced at: about 1 month ago - Pushed at: about 1 year ago - Stars: 63 - Forks: 5

chirag-agarwall/VOG

Estimating Example Difficulty using Variance of Gradients

Language: Python - Size: 246 MB - Last synced at: about 1 month ago - Pushed at: over 2 years ago - Stars: 63 - Forks: 6

breimanntools/aaanalysis

Python framework for interpretable protein prediction

Language: Jupyter Notebook - Size: 485 MB - Last synced at: 22 days ago - Pushed at: about 1 month ago - Stars: 60 - Forks: 3

mateoespinosa/cem

Repository for our NeurIPS 2022 paper "Concept Embedding Models: Beyond the Accuracy-Explainability Trade-Off" and our NeurIPS 2023 paper "Learning to Receive Help: Intervention-Aware Concept Embedding Models"

Language: Python - Size: 68 MB - Last synced at: 3 months ago - Pushed at: 3 months ago - Stars: 59 - Forks: 21

copenlu/ALPS_2021

XAI Tutorial for the Explainable AI track in the ALPS winter school 2021

Language: Jupyter Notebook - Size: 76.7 MB - Last synced at: about 1 year ago - Pushed at: over 4 years ago - Stars: 57 - Forks: 7

linkedin/TE2Rules

Python library to explain Tree Ensemble models (TE) like XGBoost, using a rule list.

Language: Python - Size: 10.9 MB - Last synced at: 5 days ago - Pushed at: about 1 year ago - Stars: 55 - Forks: 6

egyptdj/stagin

STAGIN: Spatio-Temporal Attention Graph Isomorphism Network

Language: Python - Size: 1.21 MB - Last synced at: almost 2 years ago - Pushed at: almost 2 years ago - Stars: 55 - Forks: 13

iancovert/removal-explanations

A lightweight implementation of removal-based explanations for ML models.

Language: Python - Size: 485 KB - Last synced at: over 2 years ago - Pushed at: almost 4 years ago - Stars: 53 - Forks: 10

serre-lab/Harmonization

👋 Aligning Human & Machine Vision using explainability

Language: Python - Size: 13.5 MB - Last synced at: about 1 month ago - Pushed at: almost 2 years ago - Stars: 52 - Forks: 12

WHUIR/CARP

The implementation of “A Capsule Network for Recommendation and Explaining What You Like and Dislike”, Chenliang Li, Cong Quan, Li Peng, Yunwei Qi, Yuming Deng, Libing Wu, https://dl.acm.org/citation.cfm?doid=3331184.3331216

Language: Python - Size: 36.1 KB - Last synced at: over 1 year ago - Pushed at: over 2 years ago - Stars: 51 - Forks: 19

adaamko/POTATO

XAI based human-in-the-loop framework for automatic rule-learning.

Language: Jupyter Notebook - Size: 6.07 MB - Last synced at: 7 days ago - Pushed at: 11 months ago - Stars: 49 - Forks: 8

tjiagoM/spatio-temporal-brain

A Deep Graph Neural Network Architecture for Modelling Spatio-temporal Dynamics in rs-fMRI Data

Language: Jupyter Notebook - Size: 28.5 MB - Last synced at: about 1 month ago - Pushed at: about 3 years ago - Stars: 49 - Forks: 8

serre-lab/Lens

LENS Project

Language: HTML - Size: 380 MB - Last synced at: about 22 hours ago - Pushed at: over 1 year ago - Stars: 48 - Forks: 0

csinva/iprompt

Finding semantically meaningful and accurate prompts.

Language: Jupyter Notebook - Size: 82.4 MB - Last synced at: about 1 month ago - Pushed at: over 1 year ago - Stars: 46 - Forks: 8

microsoft/responsible-ai-workshop

Responsible AI Workshop: a series of tutorials & walkthroughs to illustrate how put responsible AI into practice

Language: Jupyter Notebook - Size: 237 MB - Last synced at: 6 days ago - Pushed at: 3 months ago - Stars: 44 - Forks: 9

microsoft/augmented-interpretable-models

Interpretable and efficient predictors using pre-trained language models. Scikit-learn compatible.

Language: Jupyter Notebook - Size: 189 MB - Last synced at: 6 days ago - Pushed at: 3 months ago - Stars: 42 - Forks: 12

dmitrykazhdan/concept-based-xai

Library implementing state-of-the-art Concept-based and Disentanglement Learning methods for Explainable AI

Language: Python - Size: 110 KB - Last synced at: over 2 years ago - Pushed at: almost 3 years ago - Stars: 42 - Forks: 4

FlorianWilhelm/lda4rec

🧮 Extended Latent Dirichlet Allocation for Collaborative Filtering in Recommender Systems.

Language: Jupyter Notebook - Size: 3.12 MB - Last synced at: 12 days ago - Pushed at: about 3 years ago - Stars: 42 - Forks: 6

dylan-slack/Modeling-Uncertainty-Local-Explainability

Local explanations with uncertainty 💐!

Language: Python - Size: 7.57 MB - Last synced at: 18 days ago - Pushed at: almost 2 years ago - Stars: 40 - Forks: 15

trustyai-explainability/trustyai-explainability

TrustyAI Explainability Toolkit

Language: Java - Size: 19 MB - Last synced at: about 1 month ago - Pushed at: about 1 month ago - Stars: 39 - Forks: 42

chenhan97/TimeLlama

The official repo of TimeLlama, an instruction-finetuned Llama2 series that improve complex temporal reasoning ability.

Language: Python - Size: 13.6 MB - Last synced at: 9 days ago - Pushed at: over 1 year ago - Stars: 37 - Forks: 6

ismailuddin/gradcam-tensorflow-2

🧰 Grad-CAM implementation using TensorFlow 2.X code. Including guided Grad-CAM and counterfactuals.

Language: Jupyter Notebook - Size: 1.48 MB - Last synced at: about 1 year ago - Pushed at: over 4 years ago - Stars: 37 - Forks: 4

csinva/interpretable-embeddings

Interpretable text embeddings by asking LLMs yes/no questions (NeurIPS 2024)

Language: Python - Size: 145 MB - Last synced at: about 1 month ago - Pushed at: 7 months ago - Stars: 36 - Forks: 2

BirkhoffG/Explainable-ML-Papers

A list of research papers of explainable machine learning.

Size: 13.7 KB - Last synced at: about 1 month ago - Pushed at: almost 4 years ago - Stars: 36 - Forks: 3

lsh0520/RGCL

Ratioanle-aware Graph Contrastive Learning codebase

Language: Python - Size: 7.19 MB - Last synced at: about 1 year ago - Pushed at: almost 2 years ago - Stars: 35 - Forks: 2

zbr17/AVSL

[CVPR 2022] Official PyTorch implementation for Attributable Visual Similarity Learning

Language: Python - Size: 402 KB - Last synced at: about 1 month ago - Pushed at: over 2 years ago - Stars: 35 - Forks: 5

yc015/scene-representation-diffusion-model

Linear probe found representations of scene attributes in a text-to-image diffusion model

Language: Jupyter Notebook - Size: 73.7 MB - Last synced at: 11 months ago - Pushed at: 11 months ago - Stars: 34 - Forks: 4

koo-ec/Awesome-LLM-Explainability

A curated list of explainability-related papers, articles, and resources focused on Large Language Models (LLMs). This repository aims to provide researchers, practitioners, and enthusiasts with insights into the explainability implications, challenges, and advancements surrounding these powerful models.

Size: 764 KB - Last synced at: 11 days ago - Pushed at: 11 days ago - Stars: 33 - Forks: 1

JGalego/awesome-safety-critical-ai

A curated list of references on the role of AI in safety-critical systems ⚠️

Language: JavaScript - Size: 10.6 MB - Last synced at: about 12 hours ago - Pushed at: 10 days ago - Stars: 31 - Forks: 11

ModelOriented/ArenaR

Data generator for Arena - interactive XAI dashboard

Language: R - Size: 6.56 MB - Last synced at: 6 days ago - Pushed at: over 4 years ago - Stars: 31 - Forks: 4

pyartemis/artemis

A Python package with explanation methods for extraction of feature interactions from predictive models

Language: Jupyter Notebook - Size: 16.6 MB - Last synced at: 5 days ago - Pushed at: over 1 year ago - Stars: 30 - Forks: 0

koriavinash1/BioExp

Explainability of Deep Learning Models

Language: Python - Size: 542 MB - Last synced at: about 1 month ago - Pushed at: about 2 years ago - Stars: 29 - Forks: 5

oracle-samples/automlx

This repository contains demo notebooks (sample code) for the AutoMLx (automated machine learning and explainability) package from Oracle Labs.

Size: 14.6 MB - Last synced at: 7 days ago - Pushed at: 4 months ago - Stars: 28 - Forks: 5

LCS2-IIITD/Hyphen

[NeurIPS 2022 Oral (Spotlight)] Public Wisdom Matters! Discourse-Aware Hyperbolic Fourier Co-Attention for Social-Text Classification

Language: Python - Size: 486 MB - Last synced at: 5 months ago - Pushed at: 5 months ago - Stars: 27 - Forks: 1

FarnoushRJ/MambaLRP

[NeurIPS 2024] Official implementation of the paper "MambaLRP: Explaining Selective State Space Sequence Models".

Language: Python - Size: 7.5 MB - Last synced at: 7 months ago - Pushed at: 7 months ago - Stars: 27 - Forks: 3

AI4LIFE-GROUP/LLM_Explainer

Code for paper: Are Large Language Models Post Hoc Explainers?

Language: Jupyter Notebook - Size: 105 MB - Last synced at: 8 months ago - Pushed at: 11 months ago - Stars: 24 - Forks: 4

cloudera/CML_AMP_Explainability_LIME_SHAP

Learn how to explain ML models using LIME and SHAP.

Language: Jupyter Notebook - Size: 4.65 MB - Last synced at: 1 day ago - Pushed at: over 1 year ago - Stars: 24 - Forks: 11

jjbrophy47/tree_influence

Influence Estimation for Gradient-Boosted Decision Trees

Language: Python - Size: 5.38 MB - Last synced at: 9 months ago - Pushed at: about 1 year ago - Stars: 23 - Forks: 9

giacoballoccu/explanation-quality-recsys

Post Processing Explanations Paths in Path Reasoning Recommender Systems with Knowledge Graphs

Language: Python - Size: 900 KB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 23 - Forks: 6

vsubbian/WindowSHAP

A model-agnostic framework for explaining time-series classifiers using Shapley values

Language: Jupyter Notebook - Size: 269 KB - Last synced at: 18 days ago - Pushed at: over 1 year ago - Stars: 21 - Forks: 9

Related Topics
explainable-ai 139 interpretability 136 machine-learning 116 xai 72 deep-learning 70 explainable-ml 61 ai 39 artificial-intelligence 32 pytorch 32 interpretable-machine-learning 30 python 30 shap 27 explainable-artificial-intelligence 22 lime 20 visualization 20 data-science 19 ml 19 interpretable-ai 19 classification 18 nlp 15 neural-network 14 interpretable-ml 13 interpretable-deep-learning 13 shapley 12 neural-networks 12 computer-vision 11 counterfactual-explanations 10 deep-neural-networks 10 transformers 10 fairness 10 time-series 10 natural-language-processing 10 image-classification 10 graph-neural-networks 9 human-computer-interaction 9 iml 9 large-language-models 9 feature-importance 9 gradcam 9 random-forest 8 responsible-ai 8 explainable-machine-learning 8 xgboost 8 transparency 8 xai-library 8 text-classification 8 bias 7 llm 7 medical-imaging 7 reinforcement-learning 7 hci 7 llms 7 concept-based-explanations 7 fairness-ai 6 algorithmic-transparency 6 artificial-intelligence-algorithms 6 bayesian 6 scikit-learn 6 bayesian-statistics 6 bkt 6 human-ai 6 interactive-visualizations 6 concept-based-models 6 explanations 6 robustness 6 transformer 6 regression 6 shapley-value 6 language-model 6 cnn 6 tensorflow 5 javascript 5 html 5 css 5 privacy 5 feature-attribution 5 security 5 saliency 5 gnn 5 fairness-ml 5 machinelearning 5 feature-engineering 5 evaluation 4 awesome-list 4 concepts 4 awesome 4 evaluation-metrics 4 trustworthy-ai 4 captum 4 benchmark 4 gradient-boosting 4 grad-cam 4 attention 4 huggingface 4 dataset 4 convolutional-neural-networks 4 recourse 4 chatgpt 4 benchmarking 4 explainable-deepneuralnetwork 4