Jayneel Parekh

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All published works

Action	Title	Year	Authors
+ PDF Chat	One Wave to Explain Them All: A Unifying Perspective on Post-hoc Explainability	2024	Gabriel Kasmi Amandine Brunetto Thomas Fel Jayneel Parekh
+ PDF Chat	Restyling Unsupervised Concept Based Interpretable Networks with Generative Models	2024	Jayneel Parekh Quentin Bouniot Pavlo Mozharovskyi Alasdair Newson Florence d’Alché–Buc
+	Tackling Interpretability in Audio Classification Networks With Non-negative Matrix Factorization	2024	Jayneel Parekh Sanjeel Parekh Pavlo Mozharovskyi Gaël Richard Florence d’Alché–Buc
+	Tackling Interpretability in Audio Classification Networks with Non-negative Matrix Factorization	2023	Jayneel Parekh Sanjeel Parekh Pavlo Mozharovskyi Gaël Richard Florence d’Alché–Buc
+	Listen to Interpret: Post-hoc Interpretability for Audio Networks with NMF	2022	Jayneel Parekh Sanjeel Parekh Pavlo Mozharovskyi Florence d’Alch ́e-Buc Ga ̈el Richard
+	Listen to Interpret: Post-hoc Interpretability for Audio Networks with NMF	2022	Jayneel Parekh Sanjeel Parekh Pavlo Mozharovskyi Florence d’Alché–Buc Gaël Richard
+ PDF Chat	A Framework to Learn with Interpretation	2021	Jayneel Parekh Pavlo Mozharovskyi Florence d’Alché–Buc
+ PDF Chat	A Framework to Learn with Interpretation	2021	Jayneel Parekh Pavlo Mozharovskyi Florence d'Alché-Buc
+ PDF Chat	Speech-To-Singing Conversion in an Encoder-Decoder Framework	2020	Jayneel Parekh Preeti Rao Yi‐Hsuan Yang
+ PDF Chat	Flexible and Context-Specific AI Explainability: A Multidisciplinary Approach	2020	Valérie Beaudouin Isabelle Bloch David Bounie Stéphan Clémençon Florence d’Alché–Buc James Eagan Winston Maxwell Pavlo Mozharovskyi Jayneel Parekh
+	Speech-to-Singing Conversion in an Encoder-Decoder Framework	2020	Jayneel Parekh Preeti Rao Yi‐Hsuan Yang
+ PDF Chat	Flexible and Context-Specific AI Explainability: A Multidisciplinary Approach	2020	Valérie Beaudouin Isabelle Bloch David Bounie Stéphan Clémençon Florence d’Alché–Buc James Eagan Winston Maxwell Pavlo Mozharovskyi Jayneel Parekh
+	A Framework to Learn with Interpretation	2020	Jayneel Parekh Pavlo Mozharovskyi Florence d'Alché-Buc

Common Coauthors

Coauthor	Papers Together
Pavlo Mozharovskyi	10
Florence d’Alché–Buc	7
Sanjeel Parekh	4
Gaël Richard	3
David Bounie	2
Florence d'Alché-Buc	2
Preeti Rao	2
Isabelle Bloch	2
Stéphan Clémençon	2
Yi‐Hsuan Yang	2
James Eagan	2
Winston Maxwell	2
Quentin Bouniot	1
Valérie Beaudouin	1
Thomas Fel	1
Ga ̈el Richard	1
Gabriel Kasmi	1
Valérie Beaudouin	1
Florence d’Alch ́e-Buc	1
Amandine Brunetto	1
Alasdair Newson	1

Commonly Cited References

Action	Title	Year	Authors	# of times referenced
+ PDF Chat	Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization	2017	Ramprasaath R. Selvaraju Michael Cogswell Abhishek Das Ramakrishna Vedantam Devi Parikh Dhruv Batra	5
+	Methods for interpreting and understanding deep neural networks	2017	Grégoire Montavon Wojciech Samek Klaus‐Robert Müller	4
+ PDF Chat	The (Un)reliability of Saliency Methods	2019	Pieter-Jan Kindermans Sara Hooker Julius Adebayo Maximilian Alber Kristof T. Schütt Sven Dähne Dumitru Erhan Been Kim	4
+ PDF Chat	Generating Visual Explanations	2016	Lisa Anne Hendricks Zeynep Akata Marcus Rohrbach Jeff Donahue Bernt Schiele Trevor Darrell	4
+	A Unified Approach to Interpreting Model Predictions	2017	Scott Lundberg Su‐In Lee	4
+	Striving for Simplicity: The All Convolutional Net	2014	Jost Tobias Springenberg Alexey Dosovitskiy Thomas Brox Martin Riedmiller	3
+	“Why Should I Trust You?”: Explaining the Predictions of Any Classifier	2016	Marco Ribeiro Sameer Singh Carlos Guestrin	3
+ PDF Chat	Explaining A Black-box By Using A Deep Variational Information Bottleneck Approach	2021	Seojin Bang Pengtao Xie Heewook Lee Wei Wu Eric P. Xing	3
+	Towards Robust Interpretability with Self-Explaining Neural Networks	2018	David Alvarez-Melis Tommi Jaakkola	3
+	Towards Automatic Concept-based Explanations	2019	Amirata Ghorbani James Wexler James Zou Been Kim	3
+ PDF Chat	Interpretable machine learning: Fundamental principles and 10 grand challenges	2022	Cynthia Rudin Chaofan Chen Zhi Chen Haiyang Huang Lesia Semenova Chudi Zhong	3
+ PDF Chat	Visualizing Deep Convolutional Neural Networks Using Natural Pre-images	2016	Aravindh Mahendran Andrea Vedaldi	3
+ PDF Chat	Interpretable Convolutional Neural Networks	2018	Quanshi Zhang Ying Wu Song‐Chun Zhu	3
+	Contextual Explanation Networks	2017	Maruan Al-Shedivat Avinava Dubey Eric P. Xing	2
+	Understanding Neural Networks Through Deep Visualization	2015	Jason Yosinski Jeff Clune Anh Mai Nguyen Thomas J. Fuchs Hod Lipson	2
+	SONYC-UST-V2: An Urban Sound Tagging Dataset with Spatiotemporal Context	2020	Mark Cartwright Jason Cramer Ana Elisa Méndez Méndez Yu Wang Ho-Hsiang Wu Vincent Lostanlen Magdalena Fuentes Graham Dove Charlie Mydlarz Justin Salamon	2
+	Concept Bottleneck Models	2020	Pang Wei Koh Thao Nguyen Yew Siang Tang Stephen Mussmann Emma Pierson Been Kim Percy Liang	2
+ PDF Chat	Concept whitening for interpretable image recognition	2020	Zhi Chen Yijie Bei Cynthia Rudin	2
+ PDF Chat	Towards explainable deep neural networks (xDNN)	2020	Plamen Angelov Eduardo Soares	2
+ PDF Chat	Reliable Local Explanations for Machine Listening	2020	Saumitra Mishra Emmanouil Benetos Bob L. Sturm Simon Dixon	2
+	How to Explain Individual Classification Decisions	2009	David Baehrens Timon Schroeter Stefan Harmeling Motoaki Kawanabe Katja Hansen Klaus‐Robert Müller	2
+	Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps	2013	Karen Simonyan Andrea Vedaldi Andrew Zisserman	2
+ PDF Chat	General notions of statistical depth function	2000	Robert Serfling Yijun Zuo	2
+ PDF Chat	Identify, Locate and Separate: Audio-Visual Object Extraction in Large Video Collections Using Weak Supervision	2019	Sanjeel Parekh Alexey Ozerov Slim Essid Ngoc Q. K. Duong Patrick Pérez Gaël Richard	2
+	A Neural Representation of Sketch Drawings	2017	David Ha Douglas Eck	2
+	A Universal Music Translation Network	2018	Noam Mor Lior Wolf Adam Polyak Yaniv Taigman	2
+ PDF Chat	Identity Mappings in Deep Residual Networks	2016	Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun	2
+	This Looks Like That: Deep Learning for Interpretable Image Recognition	2018	Chaofan Chen Oscar Li Chaofan Tao Alina Jade Barnett Jonathan K. Su Cynthia Rudin	2
+	Singing Style Transfer Using Cycle-Consistent Boundary Equilibrium Generative Adversarial Networks	2018	Chengwei Wu Jen-Yu Liu Yi‐Hsuan Yang Jyh‐Shing Roger Jang	2
+	Interpreting and Explaining Deep Neural Networks for Classification of Audio Signals	2018	Sören Becker Marcel R. Ackermann Sebastian Lapuschkin Klaus‐Robert Müller Wojciech Samek	2
+	Towards A Rigorous Science of Interpretable Machine Learning	2017	Finale Doshi‐Velez Been Kim	2
+ PDF Chat	Audio Style Transfer	2018	Eric Grinstein Ngoc Q. K. Duong Alexey Ozerov Patrick Pérez	2
+ PDF Chat	Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks	2017	Jun-Yan Zhu Taesung Park Phillip Isola Alexei A. Efros	2
+ PDF Chat	Deep Learning for Case-Based Reasoning Through Prototypes: A Neural Network That Explains Its Predictions	2018	Oscar Li Hao Liu Chaofan Chen Cynthia Rudin	2
+ PDF Chat	Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI	2019	Alejandro Barredo Arrieta Natalia Díaz-Rodríguez Javier Del Ser Adrien Bennetot Siham Tabik Alberto Barbado Salvador García Sergio Gil-López Daniel Molina Richard Benjamins	2
+	Learning to Explain: An Information-Theoretic Perspective on Model Interpretation	2018	Jianbo Chen Le Song Martin J. Wainwright Michael I. Jordan	2
+ PDF Chat	Vocal Melody Extraction Using Patch-Based CNN	2018	Li Su	2
+	Toward Interpretable Music Tagging with Self-Attention	2019	Minz Won Sanghyuk Chun Xavier Serra	2
+	Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms	2017	Xiao Han Kashif Rasul Roland Vollgraf	2
+ PDF Chat	Deep recurrent NMF for speech separation by unfolding iterative thresholding	2017	Scott Wisdom Thomas A. Powers J.W. Pitton Les Atlas	2
+ PDF Chat	A causal framework for explaining the predictions of black-box sequence-to-sequence models	2017	David Alvarez-Melis Tommi Jaakkola	2
+	SuBiC: A Supervised, Structured Binary Code for Image Search	2017	Himalaya Jain Joaquin Zepeda Patrick Pérez Rémi Gribonval	2
+	Instance Normalization: The Missing Ingredient for Fast Stylization	2016	Dmitry Ulyanov Andrea Vedaldi Victor Lempitsky	2
+	Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling	2014	Jun‐Young Chung Çaǧlar Gülçehre Kyunghyun Cho Yoshua Bengio	2
+ PDF Chat	Learning to Separate Object Sounds by Watching Unlabeled Video	2018	Ruohan Gao Rogério Feris Kristen Grauman	2
+ PDF Chat	Knowledge Transfer from Weakly Labeled Audio Using Convolutional Neural Network for Sound Events and Scenes	2018	Anurag Kumar Maksim Khadkevich Christian Fügen	2
+	Dilated Convolution with Dilated GRU for Music Source Separation	2019	Jen-Yu Liu Yi‐Hsuan Yang	2
+	GANSynth: Adversarial Neural Audio Synthesis	2019	Jesse Engel Kumar Krishna Agrawal Shuo Chen Ishaan Gulrajani Chris Donahue Adam P. Roberts	2
+	Conditional End-to-End Audio Transforms	2018	Albert Haque Michelle Guo Prateek Verma	2
+ PDF Chat	Score and Lyrics-Free Singing Voice Generation	2019	Jen-Yu Liu Yuhua Chen Yin-Cheng Yeh Yi‐Hsuan Yang	2