Thomas FEL
22 papers · 2021–2025 · 6 venues · across top CS/AI venues
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Deep Learning
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(6)
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explainable ai
(3)
deep neural network
(3)
attribution method
(3)
representation learning
(3)
visual feature
(3)
object recognition
(2)
neural harmonizer
(2)
concept importance
(2)
concept-based explanation
(2)
image generation
(2)
explainability method
(2)
concept extraction
(2)
neural network interpretability
(2)
shortcut learning
(2)
image classification
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sensitivity analysis
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prototype learning
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Papers
An Adaptive Orthogonal Convolution Scheme for Efficient and Flexible CNN Architectures
ICML 2025
Archetypal SAE: Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Models
ICML 2025
One Wave To Explain Them All: A Unifying Perspective On Feature Attribution
ICML 2025
Unearthing Skill-level Insights for Understanding Trade-offs of Foundation Models
ICLR 2025
Universal Sparse Autoencoders: Interpretable Cross-Model Concept Alignment
ICML 2025
Latent Representation Matters: Human-like Sketches in One-shot Drawing Tasks
NeurIPS 2024
On the Foundations of Shortcut Learning
ICLR 2024
Saliency strikes back: How filtering out high frequencies improves white-box explanations
ICML 2024
Understanding Visual Feature Reliance through the Lens of Complexity
NeurIPS 2024
A Holistic Approach to Unifying Automatic Concept Extraction and Concept Importance Estimation
NeurIPS 2023
COCKATIEL: COntinuous Concept ranKed ATtribution with Interpretable ELements for explaining neural net classifiers on NLP
ACL 2023
CRAFT: Concept Recursive Activation FacTorization for Explainability
CVPR 2023
Diffusion Models as Artists: Are we Closing the Gap between Humans and Machines?
ICML 2023
Don't Lie to Me! Robust and Efficient Explainability With Verified Perturbation Analysis
CVPR 2023
On the explainable properties of 1-Lipschitz Neural Networks: An Optimal Transport Perspective
NeurIPS 2023
Performance-optimized deep neural networks are evolving into worse models of inferotemporal visual cortex
NeurIPS 2023
Unlocking Feature Visualization for Deep Network with MAgnitude Constrained Optimization
NeurIPS 2023
Harmonizing the object recognition strategies of deep neural networks with humans
NeurIPS 2022
How Good Is Your Explanation? Algorithmic Stability Measures To Assess the Quality of Explanations for Deep Neural Networks
WACV 2022
Making Sense of Dependence: Efficient Black-box Explanations Using Dependence Measure
NeurIPS 2022
What I Cannot Predict, I Do Not Understand: A Human-Centered Evaluation Framework for Explainability Methods
NeurIPS 2022
Look at the Variance! Efficient Black-box Explanations with Sobol-based Sensitivity Analysis
NeurIPS 2021