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07/18/2023
Gradient strikes back: How filtering out high frequencies improves explanations
Recent years have witnessed an explosion in the development of novel pre...
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06/11/2023
Unlocking Feature Visualization for Deeper Networks with MAgnitude Constrained Optimization
Feature visualization has gained substantial popularity, particularly af...
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06/11/2023
A Holistic Approach to Unifying Automatic Concept Extraction and Concept Importance Estimation
In recent years, concept-based approaches have emerged as some of the mo...
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06/06/2023
Performance-optimized deep neural networks are evolving into worse models of inferotemporal visual cortex
One of the most impactful findings in computational neuroscience over th...
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06/05/2023
Adversarial alignment: Breaking the trade-off between the strength of an attack and its relevance to human perception
Deep neural networks (DNNs) are known to have a fundamental sensitivity ...
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05/11/2023
COCKATIEL: COntinuous Concept ranKed ATtribution with Interpretable ELements for explaining neural net classifiers on NLP tasks
Transformer architectures are complex and their use in NLP, while it has...
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04/12/2023
Confident Object Detection via Conformal Prediction and Conformal Risk Control: an Application to Railway Signaling
Deploying deep learning models in real-world certified systems requires ...
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01/27/2023
Diffusion Models as Artists: Are we Closing the Gap between Humans and Machines?
An important milestone for AI is the development of algorithms that can ...
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01/26/2023
Conformal Prediction for Trustworthy Detection of Railway Signals
We present an application of conformal prediction, a form of uncertainty...
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11/17/2022
CRAFT: Concept Recursive Activation FacTorization for Explainability
Attribution methods are a popular class of explainability methods that u...
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11/08/2022
Harmonizing the object recognition strategies of deep neural networks with humans
The many successes of deep neural networks (DNNs) over the past decade h...
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08/17/2022
Conviformers: Convolutionally guided Vision Transformer
Vision transformers are nowadays the de-facto preference for image class...
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06/14/2022
When adversarial attacks become interpretable counterfactual explanations
We argue that, when learning a 1-Lipschitz neural network with the dual ...
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06/13/2022
Making Sense of Dependence: Efficient Black-box Explanations Using Dependence Measure
This paper presents a new efficient black-box attribution method based o...
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06/09/2022
Xplique: A Deep Learning Explainability Toolbox
Today's most advanced machine-learning models are hardly scrutable. The ...
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02/15/2022
Don't Lie to Me! Robust and Efficient Explainability with Verified Perturbation Analysis
A variety of methods have been proposed to try to explain how deep neura...
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12/06/2021
What I Cannot Predict, I Do Not Understand: A Human-Centered Evaluation Framework for Explainability Methods
A multitude of explainability methods and theoretical evaluation scores ...
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11/07/2021
Look at the Variance! Efficient Black-box Explanations with Sobol-based Sensitivity Analysis
We describe a novel attribution method which is grounded in Sensitivity ...
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09/07/2020