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README.md

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- [Xplique](https://github.com/deel-ai/xplique) a Python library exclusively dedicated to explaining neural networks.
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- [Influenciae](https://github.com/deel-ai/influenciae) Python toolkit dedicated to computing influence values for the discovery of potentially problematic samples in a dataset.
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- [deel-torchlip](https://github.com/deel-ai/deel-torchlip) a Python library for training k-Lipschitz neural networks on PyTorch.
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- [oodeel](https://github.com/deel-ai/oodeel) a Python library for post-hoc deep OOD (Out-of-Distribution) detection on already trained neural network image classifiers
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- [deel-TorchLip](https://github.com/deel-ai/deel-torchlip) a Python library for training k-Lipschitz neural networks on PyTorch.
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- [Oodeel](https://github.com/deel-ai/oodeel) a Python library for post-hoc deep OOD (Out-of-Distribution) detection on already trained neural network image classifiers
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- [DEEL White paper](https://arxiv.org/abs/2103.10529) a summary of the DEEL team on the challenges of certifiable AI and the role of data quality, representativity and explainability for this purpose.
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## 🙏 Acknowledgments

docs/index.md

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- [Xplique](https://github.com/deel-ai/xplique) a Python library exclusively dedicated to explaining neural networks.
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- [Influenciae](https://github.com/deel-ai/influenciae) Python toolkit dedicated to computing influence values for the discovery of potentially problematic samples in a dataset.
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- [deel-torchlip](https://github.com/deel-ai/deel-torchlip) a Python library for training k-Lipschitz neural networks on PyTorch.
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- [oodeel](https://github.com/deel-ai/oodeel) a Python library for post-hoc deep OOD (Out-of-Distribution) detection on already trained neural network image classifiers
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- [deel-TorchLip](https://github.com/deel-ai/deel-torchlip) a Python library for training k-Lipschitz neural networks on PyTorch.
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- [Oodeel](https://github.com/deel-ai/oodeel) a Python library for post-hoc deep OOD (Out-of-Distribution) detection on already trained neural network image classifiers
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- [DEEL White paper](https://arxiv.org/abs/2103.10529) a summary of the DEEL team on the challenges of certifiable AI and the role of data quality, representativity and explainability for this purpose.
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## 🙏 Acknowledgments

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