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
