79 Open source projects that are alternatives of or similar to Deeplift

Adaptive, interpretable wavelets across domains (NeurIPS 2021)

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A collection of research materials on explainable AI/ML

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Techniques & resources for training interpretable ML models, explaining ML models, and debugging ML models.

A curated list of awesome Fairness in AI resources

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Awesome Resources for Advanced Computer Vision Topics

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A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models.

Code for "Distributed, Egocentric Representations of Graphs for Detecting Critical Structures" (ICML 2019)

Sample use case for Xavier AI in Healthcare conference: https://www.xavierhealth.org/ai-summit-day2/

Interesting resources related to Explainable Artificial Intelligence, Interpretable Machine Learning, Interactive Machine Learning, Human in Loop and Visual Analytics.

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

NeurIPS 2018. Linear-time model comparison tests.

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

Collection of NLP model explanations and accompanying analysis tools

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

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

Code for SPINE - Sparse Interpretable Neural Embeddings. Jhamtani H.*, Pruthi D.*, Subramanian A.*, Berg-Kirkpatrick T., Hovy E. AAAI 2018

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

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

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A curated list of awesome machine learning interpretability resources.

Concept Bottleneck Models, ICML 2020

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Model Agnostics breakDown plots

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

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Human-explainable AI.

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[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.