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Thomas Fel
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2020 – today
- 2024
- [j1]Léo Andéol, Thomas Fel, Florence de Grancey, Luca Mossina:
Conformal prediction for trustworthy detection of railway signals. AI Ethics 4(1): 157-161 (2024) - [c17]Katherine L. Hermann, Hossein Mobahi, Thomas Fel, Michael Curtis Mozer:
On the Foundations of Shortcut Learning. ICLR 2024 - [c16]Sabine Muzellec, Thomas Fel, Victor Boutin, Léo Andéol, Rufin VanRullen, Thomas Serre:
Saliency strikes back: How filtering out high frequencies improves white-box explanations. ICML 2024 - [c15]Agustin Picard, Lucas Hervier, Thomas Fel, David Vigouroux:
Influenciæ: A Library for Tracing the Influence Back to the Data-Points. xAI (4) 2024: 193-204 - [i25]Chris J. Hamblin, Thomas Fel, Srijani Saha, Talia Konkle, George A. Alvarez:
Feature Accentuation: Revealing 'What' Features Respond to in Natural Images. CoRR abs/2402.10039 (2024) - [i24]Victor Boutin, Rishav Mukherji, Aditya Agrawal, Sabine Muzellec, Thomas Fel, Thomas Serre, Rufin VanRullen:
Latent Representation Matters: Human-like Sketches in One-shot Drawing Tasks. CoRR abs/2406.06079 (2024) - [i23]Thomas Fel, Louis Béthune, Andrew Kyle Lampinen, Thomas Serre, Katherine L. Hermann:
Understanding Visual Feature Reliance through the Lens of Complexity. CoRR abs/2407.06076 (2024) - [i22]Gabriel Kasmi, Amandine Brunetto, Thomas Fel, Jayneel Parekh:
One Wave to Explain Them All: A Unifying Perspective on Post-hoc Explainability. CoRR abs/2410.01482 (2024) - [i21]Mazda Moayeri, Vidhisha Balachandran, Varun Chandrasekaran, Safoora Yousefi, Thomas Fel, Soheil Feizi, Besmira Nushi, Neel Joshi, Vibhav Vineet:
Unearthing Skill-Level Insights for Understanding Trade-Offs of Foundation Models. CoRR abs/2410.13826 (2024) - [i20]Julien Colin, Lore Goetschalckx, Thomas Fel, Victor Boutin, Jay Gopal, Thomas Serre, Nuria Oliver:
Local vs distributed representations: What is the right basis for interpretability? CoRR abs/2411.03993 (2024) - 2023
- [c14]Fanny Jourdan, Agustin Martin Picard, Thomas Fel, Laurent Risser, Jean-Michel Loubes, Nicholas Asher:
COCKATIEL: COntinuous Concept ranKed ATtribution with Interpretable ELements for explaining neural net classifiers on NLP. ACL (Findings) 2023: 5120-5136 - [c13]Léo Andéol, Thomas Fel, Florence de Grancey, Luca Mossina:
Confident Object Detection via Conformal Prediction and Conformal Risk Control: an Application to Railway Signaling. COPA 2023: 36-55 - [c12]Thomas Fel, Agustin Martin Picard, Louis Béthune, Thibaut Boissin, David Vigouroux, Julien Colin, Rémi Cadène, Thomas Serre:
CRAFT: Concept Recursive Activation FacTorization for Explainability. CVPR 2023: 2711-2721 - [c11]Thomas Fel, Melanie Ducoffe, David Vigouroux, Rémi Cadène, Mikael Capelle, Claire Nicodème, Thomas Serre:
Don't Lie to Me! Robust and Efficient Explainability with Verified Perturbation Analysis. CVPR 2023: 16153-16163 - [c10]Victor Boutin, Thomas Fel, Lakshya Singhal, Rishav Mukherji, Akash Nagaraj, Julien Colin
, Thomas Serre:
Diffusion Models as Artists: Are we Closing the Gap between Humans and Machines? ICML 2023: 2953-3002 - [c9]Thomas Fel, Victor Boutin, Louis Béthune, Rémi Cadène, Mazda Moayeri, Léo Andéol, Mathieu Chalvidal, Thomas Serre:
A Holistic Approach to Unifying Automatic Concept Extraction and Concept Importance Estimation. NeurIPS 2023 - [c8]Thomas Fel, Thibaut Boissin, Victor Boutin, Agustin Picard, Paul Novello, Julien Colin, Drew Linsley, Tom Rousseau, Rémi Cadène, Lore Goetschalckx, Laurent Gardes, Thomas Serre:
Unlocking Feature Visualization for Deep Network with MAgnitude Constrained Optimization. NeurIPS 2023 - [c7]Drew Linsley, Ivan F. Rodriguez Rodriguez, Thomas Fel, Michael Arcaro, Saloni Sharma, Margaret S. Livingstone, Thomas Serre:
Performance-optimized deep neural networks are evolving into worse models of inferotemporal visual cortex. NeurIPS 2023 - [c6]Mathieu Serrurier, Franck Mamalet, Thomas Fel, Louis Béthune, Thibaut Boissin:
On the explainable properties of 1-Lipschitz Neural Networks: An Optimal Transport Perspective. NeurIPS 2023 - [i19]Victor Boutin, Thomas Fel, Lakshya Singhal, Rishav Mukherji, Akash Nagaraj
Diffusion Models as Artists: Are we Closing the Gap between Humans and Machines? CoRR abs/2301.11722 (2023) - [i18]Léo Andéol, Thomas Fel, Florence de Grancey, Luca Mossina:
Confident Object Detection via Conformal Prediction and Conformal Risk Control: an Application to Railway Signaling. CoRR abs/2304.06052 (2023) - [i17]Fanny Jourdan, Agustin Martin Picard, Thomas Fel, Laurent Risser, Jean-Michel Loubes, Nicholas Asher:
COCKATIEL: COntinuous Concept ranKed ATtribution with Interpretable ELements for explaining neural net classifiers on NLP tasks. CoRR abs/2305.06754 (2023) - [i16]Drew Linsley, Pinyuan Feng, Thibaut Boissin, Alekh Karkada Ashok, Thomas Fel, Stephanie Olaiya, Thomas Serre:
Adversarial alignment: Breaking the trade-off between the strength of an attack and its relevance to human perception. CoRR abs/2306.03229 (2023) - [i15]Drew Linsley, Ivan Felipe Rodríguez, Thomas Fel, Michael Arcaro, Saloni Sharma, Margaret S. Livingstone, Thomas Serre:
Performance-optimized deep neural networks are evolving into worse models of inferotemporal visual cortex. CoRR abs/2306.03779 (2023) - [i14]Thomas Fel, Thibaut Boissin, Victor Boutin, Agustin Martin Picard, Paul Novello, Julien Colin
Unlocking Feature Visualization for Deeper Networks with MAgnitude Constrained Optimization. CoRR abs/2306.06805 (2023) - [i13]Thomas Fel, Victor Boutin, Mazda Moayeri, Rémi Cadène, Louis Béthune, Léo Andéol, Mathieu Chalvidal, Thomas Serre:
A Holistic Approach to Unifying Automatic Concept Extraction and Concept Importance Estimation. CoRR abs/2306.07304 (2023) - [i12]Sabine Muzellec, Léo Andéol, Thomas Fel, Rufin VanRullen, Thomas Serre:
Gradient strikes back: How filtering out high frequencies improves explanations. CoRR abs/2307.09591 (2023) - [i11]Katherine L. Hermann, Hossein Mobahi, Thomas Fel, Michael C. Mozer:
On the Foundations of Shortcut Learning. CoRR abs/2310.16228 (2023) - 2022
- [c5]Julien Colin, Thomas Fel, Rémi Cadène, Thomas Serre:
What I Cannot Predict, I Do Not Understand: A Human-Centered Evaluation Framework for Explainability Methods. NeurIPS 2022 - [c4]Thomas Fel, Ivan F. Rodriguez Rodriguez, Drew Linsley, Thomas Serre:
Harmonizing the object recognition strategies of deep neural networks with humans. NeurIPS 2022 - [c3]Paul Novello, Thomas Fel, David Vigouroux:
Making Sense of Dependence: Efficient Black-box Explanations Using Dependence Measure. NeurIPS 2022 - [c2]Thomas Fel, David Vigouroux, Rémi Cadène, Thomas Serre:
How Good is your Explanation? Algorithmic Stability Measures to Assess the Quality of Explanations for Deep Neural Networks. WACV 2022: 1565-1575 - [i10]Thomas Fel, Melanie Ducoffe, David Vigouroux, Rémi Cadène, Mikael Capelle, Claire Nicodeme, Thomas Serre:
Don't Lie to Me! Robust and Efficient Explainability with Verified Perturbation Analysis. CoRR abs/2202.07728 (2022) - [i9]Thomas Fel, Lucas Hervier, David Vigouroux, Antonin Poché, Justin Plakoo, Rémi Cadène, Mathieu Chalvidal, Julien Colin
Xplique: A Deep Learning Explainability Toolbox. CoRR abs/2206.04394 (2022) - [i8]Paul Novello, Thomas Fel, David Vigouroux:
Making Sense of Dependence: Efficient Black-box Explanations Using Dependence Measure. CoRR abs/2206.06219 (2022) - [i7]Mathieu Serrurier, Franck Mamalet, Thomas Fel, Louis Béthune, Thibaut Boissin:
When adversarial attacks become interpretable counterfactual explanations. CoRR abs/2206.06854 (2022) - [i6]Mohit Vaishnav, Thomas Fel, Ivan Felipe Rodríguez, Thomas Serre:
Conviformers: Convolutionally guided Vision Transformer. CoRR abs/2208.08900 (2022) - [i5]Thomas Fel, Ivan Felipe Rodríguez, Drew Linsley, Thomas Serre:
Harmonizing the object recognition strategies of deep neural networks with humans. CoRR abs/2211.04533 (2022) - [i4]Thomas Fel, Agustin Martin Picard, Louis Béthune, Thibaut Boissin, David Vigouroux, Julien Colin
CRAFT: Concept Recursive Activation FacTorization for Explainability. CoRR abs/2211.10154 (2022) - 2021
- [c1]Thomas Fel, Rémi Cadène, Mathieu Chalvidal, Matthieu Cord, David Vigouroux, Thomas Serre:
Look at the Variance! Efficient Black-box Explanations with Sobol-based Sensitivity Analysis. NeurIPS 2021: 26005-26014 - [i3]Thomas Fel, Rémi Cadène, Mathieu Chalvidal, Matthieu Cord, David Vigouroux, Thomas Serre:
Look at the Variance! Efficient Black-box Explanations with Sobol-based Sensitivity Analysis. CoRR abs/2111.04138 (2021) - [i2]Thomas Fel, Julien Colin, Rémi Cadène, Thomas Serre:
What I Cannot Predict, I Do Not Understand: A Human-Centered Evaluation Framework for Explainability Methods. CoRR abs/2112.04417 (2021) - 2020
- [i1]Thomas Fel, David Vigouroux:
Representativity and Consistency Measures for Deep Neural Network Explanations. CoRR abs/2009.04521 (2020)
Coauthor Index
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