Xplique: A Deep Learning Explainability Toolbox

• URL: http://arxiv.org/abs/2206.04394v1
• Date: Thu, 9 Jun 2022 10:16:07 GMT
• Title: Xplique: A Deep Learning Explainability Toolbox
• Authors: Thomas Fel, Lucas Hervier, David Vigouroux, Antonin Poche, Justin Plakoo, Remi Cadene, Mathieu Chalvidal, Julien Colin, Thibaut Boissin, Louis Bethune, Agustin Picard, Claire Nicodeme, Laurent Gardes, Gregory Flandin, Thomas Serre
• Abstract summary: We have developed Xplique: a software library for explainability. It includes representative explainability methods as well as associated evaluation metrics. The code is licensed under the MIT license and is freely available.
• Score: 5.067377019157635
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Today's most advanced machine-learning models are hardly scrutable. The key challenge for explainability methods is to help assisting researchers in opening up these black boxes, by revealing the strategy that led to a given decision, by characterizing their internal states or by studying the underlying data representation. To address this challenge, we have developed Xplique: a software library for explainability which includes representative explainability methods as well as associated evaluation metrics. It interfaces with one of the most popular learning libraries: Tensorflow as well as other libraries including PyTorch, scikit-learn and Theano. The code is licensed under the MIT license and is freely available at github.com/deel-ai/xplique.

Related papers

• SequeL: A Continual Learning Library in PyTorch and JAX [50.33956216274694]
  SequeL is a library for Continual Learning that supports both PyTorch and JAX frameworks. It provides a unified interface for a wide range of Continual Learning algorithms, including regularization-based approaches, replay-based approaches, and hybrid approaches. We release SequeL as an open-source library, enabling researchers and developers to easily experiment and extend the library for their own purposes.
  arXiv  Detail & Related papers  (2023-04-21T10:00:22Z)
• hyperbox-brain: A Toolbox for Hyperbox-based Machine Learning Algorithms [9.061408029414455]
  hyperbox-brain is an open-source Python library implementing the leading hyperbox-based machine learning algorithms. hyperbox-brain exposes a unified API which closely follows and is compatible with the renowned scikit-learn and numpy toolboxes.
  arXiv  Detail & Related papers  (2022-10-06T06:40:07Z)
• What and How of Machine Learning Transparency: Building Bespoke Explainability Tools with Interoperable Algorithmic Components [77.87794937143511]
  This paper introduces a collection of hands-on training materials for explaining data-driven predictive models. These resources cover the three core building blocks of this technique: interpretable representation composition, data sampling and explanation generation.
  arXiv  Detail & Related papers  (2022-09-08T13:33:25Z)
• OmniXAI: A Library for Explainable AI [98.07381528393245]
  We introduce OmniXAI, an open-source Python library of eXplainable AI (XAI) It offers omni-way explainable AI capabilities and various interpretable machine learning techniques. For practitioners, the library provides an easy-to-use unified interface to generate the explanations for their applications.
  arXiv  Detail & Related papers  (2022-06-01T11:35:37Z)
• Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019 [112.36155380260655]
  This paper reports the results and post-challenge analyses of ChaLearn's AutoDL challenge series. Results show that DL methods dominated, though popular Neural Architecture Search (NAS) was impractical. A high level modular organization emerged featuring a "meta-learner", "data ingestor", "model selector", "model/learner", and "evaluator"
  arXiv  Detail & Related papers  (2022-01-11T06:21:18Z)
• Dive into Deep Learning [119.30375933463156]
  The book is drafted in Jupyter notebooks, seamlessly integrating exposition figures, math, and interactive examples with self-contained code. Our goal is to offer a resource that could (i) be freely available for everyone; (ii) offer sufficient technical depth to provide a starting point on the path to becoming an applied machine learning scientist; (iii) include runnable code, showing readers how to solve problems in practice; (iv) allow for rapid updates, both by us and also by the community at large.
  arXiv  Detail & Related papers  (2021-06-21T18:19:46Z)
• Neko: a Library for Exploring Neuromorphic Learning Rules [0.3499870393443268]
  Neko is a modular library for neuromorphic learning algorithms. It can replicate state-of-the-art algorithms and, in one case, lead to significant outperformance in accuracy and speed. Neko is an open source Python library that supports PyTorch and backends.
  arXiv  Detail & Related papers  (2021-05-01T18:50:32Z)
• Captum: A unified and generic model interpretability library for PyTorch [49.72749684393332]
  We introduce a novel, unified, open-source model interpretability library for PyTorch. The library contains generic implementations of a number of gradient and perturbation-based attribution algorithms. It can be used for both classification and non-classification models.
  arXiv  Detail & Related papers  (2020-09-16T18:57:57Z)
• PyTorch Metric Learning [37.03614011735927]
  PyTorch Metric Learning is an open source library that aims to remove this barrier for both researchers and practitioners. The modular and flexible design allows users to easily try out different combinations of algorithms in their existing code. It also comes with complete train/test, for users who want results fast.
  arXiv  Detail & Related papers  (2020-08-20T19:08:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.