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Search results for jupyter notebook explainability
explainability
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jupyter-notebook
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34 search results found
Shapash
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2,547
🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
Transformer Explainability
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1,596
[CVPR 2021] Official PyTorch implementation for Transformer Interpretability Beyond Attention Visualization, a novel method to visualize classifications by Transformer based networks.
Responsible Ai Toolbox
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1,187
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.
H1st
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781
Power Tools for AI Engineers With Deadlines
Transformer Mm Explainability
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490
[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.
Explainx
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375
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]
Timeshap
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127
TimeSHAP explains Recurrent Neural Network predictions.
Hierarchical Dnn Interpretations
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119
Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)
Robustvit
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96
[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.
Pytorch_explain
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80
PyTorch Explain: Logic Explained Networks in Python.
Deep Explanation Penalization
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74
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
Acv00
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73
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.
Contextual Ai
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72
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.
Clip_surgery
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55
CLIP Surgery for Better Explainability with Enhancement in Open-Vocabulary Tasks
Graph Network Explainability
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51
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)
Potato
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44
XAI based human-in-the-loop framework for automatic rule-learning.
Code Samples
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42
Holds code for our CVPR'23 tutorial: All Things ViTs: Understanding and Interpreting Attention in Vision.
Augmented Interpretable Models
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35
Interpretable and efficient predictors using pre-trained language models. Scikit-learn compatible.
Lda4rec
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34
🧮 Extended Latent Dirichlet Allocation for Collaborative Filtering in Recommender Systems.
Scene Representation Diffusion Model
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21
Linear probe found representations of scene attributes in a text-to-image diffusion model
Artemis
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19
A Python package with explanation methods for extraction of feature interactions from predictive models
Tradernet Crv2
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18
TraderNet-CRv2 - Combining Deep Reinforcement Learning with Technical Analysis and Trend Monitoring on Cryptocurrency Markets
Xai Scholar
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16
Cross-field empirical trends analysis of XAI literature
Gebi
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15
GEBI: Global Explanations for Bias Identification. Open source code for discovering bias in data with skin lesion dataset
Spatio Temporal Brain
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13
A Deep Graph Neural Network Architecture for Modelling Spatio-temporal Dynamics in rs-fMRI Data
Icml 2023 Route Interpret Repeat
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12
Official repository of ICML 2023 paper: Dividing and Conquering a BlackBox to a Mixture of Interpretable Models: Route, Interpret, Repeat
Bhad
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9
A Python library for Bayesian Anomaly Detection
Responsible Ai Workshop
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9
Responsible AI Workshop: a series of tutorials & walkthroughs to illustrate how put responsible AI into practice
Cml_amp_churn_prediction
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9
Build an scikit-learn model to predict churn using customer telco data.
Decompx
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9
DecompX: Explaining Transformers Decisions by Propagating Token Decomposition
Trusty Ai Sandbox
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8
A sandbox repository for the Trusty AI team
Context Probing
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7
Black-box language model explanation by context length probing
Aaanalysis
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7
Python framework for interpretable protein prediction
Explainable Qe Shared Task
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7
IST-Unbabel 2021 Submission for the Quality Estimation Shared Task
Cwox
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5
A XAI Framework to provide Contrastive Whole-output Explanation for Image Classification.
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1-34 of 34 search results
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