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2020 – today
- 2024
[j1]Jayneel Parekh, Sanjeel Parekh, Pavlo Mozharovskyi
, Gaël Richard, Florence d'Alché-Buc:
Tackling Interpretability in Audio Classification Networks With Non-negative Matrix Factorization. IEEE ACM Trans. Audio Speech Lang. Process. 32: 1392-1405 (2024)- [i7]Jayneel Parekh, Pegah Khayatan, Mustafa Shukor, Alasdair Newson, Matthieu Cord:
A Concept-Based Explainability Framework for Large Multimodal Models. CoRR abs/2406.08074 (2024) - [i6]Jayneel Parekh, Quentin Bouniot, Pavlo Mozharovskyi, Alasdair Newson, Florence d'Alché-Buc:
Restyling Unsupervised Concept Based Interpretable Networks with Generative Models. CoRR abs/2407.01331 (2024) - 2023
- [b1]Jayneel Parekh:
Un cadre flexible pour l'apprentissage automatique interprétable: application à la classification d'images et d'audio. (A Flexible Framework for Interpretable Machine Learning: application to image and audio classification). Polytechnic Institute of Paris, Palaiseau, France, 2023 - [i5]Jayneel Parekh, Sanjeel Parekh, Pavlo Mozharovskyi, Gaël Richard, Florence d'Alché-Buc:
Tackling Interpretability in Audio Classification Networks with Non-negative Matrix Factorization. CoRR abs/2305.07132 (2023) - 2022
- [c7]Jayneel Parekh, Sanjeel Parekh, Pavlo Mozharovskyi, Florence d'Alché-Buc, Gaël Richard:
Listen to Interpret: Post-hoc Interpretability for Audio Networks with NMF. NeurIPS 2022 - [i4]Jayneel Parekh, Sanjeel Parekh, Pavlo Mozharovskyi, Florence d'Alché-Buc, Gaël Richard:
Listen to Interpret: Post-hoc Interpretability for Audio Networks with NMF. CoRR abs/2202.11479 (2022) - 2021
- [c6]Jayneel Parekh, Pavlo Mozharovskyi, Florence d'Alché-Buc:
A Framework to Learn with Interpretation. NeurIPS 2021: 24273-24285 - 2020
- [c5]Valérie Beaudouin, Isabelle Bloch, David Bounie, Stéphan Clémençon, Florence d'Alché-Buc, James Eagan, Winston Maxwell, Pavlo Mozharovskyi, Jayneel Parekh:
Identifying the "right" level of explanation in a given situation. NeHuAI@ECAI 2020: 63-66 - [c4]Jayneel Parekh, Preeti Rao, Yi-Hsuan Yang:
Speech-To-Singing Conversion in an Encoder-Decoder Framework. ICASSP 2020: 261-265 - [i3]Jayneel Parekh, Preeti Rao, Yi-Hsuan Yang:
Speech-to-Singing Conversion in an Encoder-Decoder Framework. CoRR abs/2002.06595 (2020) - [i2]Valérie Beaudouin, Isabelle Bloch, David Bounie, Stéphan Clémençon, Florence d'Alché-Buc, James Eagan, Winston Maxwell, Pavlo Mozharovskyi, Jayneel Parekh:
Flexible and Context-Specific AI Explainability: A Multidisciplinary Approach. CoRR abs/2003.07703 (2020) - [i1]Jayneel Parekh, Pavlo Mozharovskyi, Florence d'Alché-Buc:
A Framework to Learn with Interpretation. CoRR abs/2010.09345 (2020)
2010 – 2019
- 2018
- [c3]Jayneel Parekh, Harshvardhan Tibrewal, Sanjeel Parekh:
Deep Pairwise Classification and Ranking for Predicting Media Interestingness. ICMR 2018: 428-433 - 2017
- [c2]Jayneel Parekh, Harshvardhan Tibrewal, Sanjeel Parekh:
The IITB Predicting Media Interestingness System for MediaEval 2017. MediaEval 2017 - 2016
- [c1]Jayneel Parekh, Sanjeel Parekh:
The MLPBOON Predicting Media Interestingness System for MediaEval 2016. MediaEval 2016
Coauthor Index
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