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Sander Dieleman
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2020 – today
- 2024
- [i29]Alice Baird, Rachel Manzelli, Panagiotis Tzirakis, Chris Gagne, Haoqi Li, Sadie Allen, Sander Dieleman, Brian Kulis, Shrikanth S. Narayanan, Alan Cowen:
The NeurIPS 2023 Machine Learning for Audio Workshop: Affective Audio Benchmarks and Novel Data. CoRR abs/2403.14048 (2024) - [i28]Jason Baldridge, Jakob Bauer, Mukul Bhutani, Nicole Brichtova, Andrew Bunner, Kelvin Chan, Yichang Chen, Sander Dieleman, Yuqing Du, Zach Eaton-Rosen, Hongliang Fei, Nando de Freitas, Yilin Gao, Evgeny Gladchenko, Sergio Gómez Colmenarejo, Mandy Guo, Alex Haig, Will Hawkins, Hexiang Hu, Huilian Huang, Tobenna Peter Igwe, Christos Kaplanis, Siavash Khodadadeh, Yelin Kim, Ksenia Konyushkova, Karol Langner, Eric Lau, Shixin Luo, Sona Mokrá, Henna Nandwani, Yasumasa Onoe, Aäron van den Oord, Zarana Parekh, Jordi Pont-Tuset, Hang Qi, Rui Qian, Deepak Ramachandran, Poorva Rane, Abdullah Rashwan, Ali Razavi, Robert Riachi, Hansa Srinivasan, Srivatsan Srinivasan, Robin Strudel, Benigno Uria, Oliver Wang, Su Wang, Austin Waters, Chris Wolff, Auriel Wright, Zhisheng Xiao, Hao Xiong, Keyang Xu, Marc van Zee, Junlin Zhang, Katie Zhang, Wenlei Zhou, Konrad Zolna, Ola Aboubakar, Canfer Akbulut, Oscar Akerlund, Isabela Albuquerque, Nina Anderson, Marco Andreetto, Lora Aroyo, Ben Bariach, David Barker, Sherry Ben, Dana Berman, Courtney Biles, Irina Blok, Pankil Botadra, Jenny Brennan, Karla Brown, John Buckley, Rudy Bunel, Elie Bursztein, Christina Butterfield, Ben Caine, Viral Carpenter, Norman Casagrande, Ming-Wei Chang, Solomon Chang, Shamik Chaudhuri, Tony Chen, John Choi, Dmitry Churbanau, Nathan Clement, Matan Cohen, Forrester Cole, Mikhail Dektiarev, Vincent Du, Praneet Dutta, Tom Eccles, Ndidi Elue, Ashley Feden, Shlomi Fruchter, Frankie Garcia, Roopal Garg:
Imagen 3. CoRR abs/2408.07009 (2024) - 2023
- [c27]Yilun Du, Conor Durkan, Robin Strudel, Joshua B. Tenenbaum, Sander Dieleman, Rob Fergus, Jascha Sohl-Dickstein, Arnaud Doucet, Will Sussman Grathwohl:
Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMC. ICML 2023: 8489-8510 - [i27]Yilun Du, Conor Durkan, Robin Strudel, Joshua B. Tenenbaum, Sander Dieleman, Rob Fergus, Jascha Sohl-Dickstein, Arnaud Doucet, Will Grathwohl:
Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMC. CoRR abs/2302.11552 (2023) - 2022
- [c26]Luyu Wang, Pauline Luc, Yan Wu, Adrià Recasens, Lucas Smaira, Andrew Brock, Andrew Jaegle, Jean-Baptiste Alayrac, Sander Dieleman, João Carreira, Aäron van den Oord:
Towards Learning Universal Audio Representations. ICASSP 2022: 4593-4597 - [c25]Curtis Hawthorne, Andrew Jaegle, Catalina Cangea, Sebastian Borgeaud, Charlie Nash, Mateusz Malinowski, Sander Dieleman, Oriol Vinyals, Matthew M. Botvinick, Ian Simon, Hannah Sheahan, Neil Zeghidour, Jean-Baptiste Alayrac, João Carreira, Jesse H. Engel:
General-purpose, long-context autoregressive modeling with Perceiver AR. ICML 2022: 8535-8558 - [i26]Curtis Hawthorne, Andrew Jaegle, Catalina Cangea, Sebastian Borgeaud, Charlie Nash, Mateusz Malinowski, Sander Dieleman, Oriol Vinyals, Matthew M. Botvinick, Ian Simon, Hannah Sheahan, Neil Zeghidour, Jean-Baptiste Alayrac, João Carreira, Jesse H. Engel:
General-purpose, long-context autoregressive modeling with Perceiver AR. CoRR abs/2202.07765 (2022) - [i25]Pierre H. Richemond, Sander Dieleman, Arnaud Doucet:
Categorical SDEs with Simplex Diffusion. CoRR abs/2210.14784 (2022) - [i24]Robin Strudel, Corentin Tallec, Florent Altché, Yilun Du, Yaroslav Ganin, Arthur Mensch, Will Grathwohl, Nikolay Savinov, Sander Dieleman, Laurent Sifre, Rémi Leblond:
Self-conditioned Embedding Diffusion for Text Generation. CoRR abs/2211.04236 (2022) - [i23]Sander Dieleman, Laurent Sartran, Arman Roshannai, Nikolay Savinov, Yaroslav Ganin, Pierre H. Richemond, Arnaud Doucet, Robin Strudel, Chris Dyer, Conor Durkan, Curtis Hawthorne, Rémi Leblond, Will Grathwohl, Jonas Adler:
Continuous diffusion for categorical data. CoRR abs/2211.15089 (2022) - 2021
- [c24]Jeff Donahue, Sander Dieleman, Mikolaj Binkowski, Erich Elsen, Karen Simonyan:
End-to-end Adversarial Text-to-Speech. ICLR 2021 - [c23]Charlie Nash, Jacob Menick, Sander Dieleman, Peter W. Battaglia:
Generating images with sparse representations. ICML 2021: 7958-7968 - [i22]Skanda Koppula, Victor Bapst, Marc Huertas-Company, Sam Blackwell, Agnieszka Grabska-Barwinska, Sander Dieleman, Andrea Huber, Natasha Antropova, Mikolaj Binkowski, Hannah Openshaw, Adrià Recasens, Fernando Caro, Avishai Deke, Yohan Dubois, Jesus Vega Ferrero, David C. Koo, Joel R. Primack, Trevor Back:
A Deep Learning Approach for Characterizing Major Galaxy Mergers. CoRR abs/2102.05182 (2021) - [i21]Charlie Nash, Jacob Menick, Sander Dieleman, Peter W. Battaglia:
Generating Images with Sparse Representations. CoRR abs/2103.03841 (2021) - [i20]Sander Dieleman, Charlie Nash, Jesse H. Engel, Karen Simonyan:
Variable-rate discrete representation learning. CoRR abs/2103.06089 (2021) - [i19]Luyu Wang, Pauline Luc, Yan Wu, Adrià Recasens, Lucas Smaira, Andrew Brock, Andrew Jaegle, Jean-Baptiste Alayrac, Sander Dieleman, João Carreira, Aäron van den Oord:
Towards Learning Universal Audio Representations. CoRR abs/2111.12124 (2021) - 2020
- [j4]Sageev Oore, Ian Simon, Sander Dieleman, Douglas Eck, Karen Simonyan:
This time with feeling: learning expressive musical performance. Neural Comput. Appl. 32(4): 955-967 (2020) - [c22]Mikolaj Binkowski, Jeff Donahue, Sander Dieleman, Aidan Clark, Erich Elsen, Norman Casagrande, Luis C. Cobo, Karen Simonyan:
High Fidelity Speech Synthesis with Adversarial Networks. ICLR 2020 - [c21]Jean-Baptiste Alayrac, Adrià Recasens, Rosalia Schneider, Relja Arandjelovic, Jason Ramapuram, Jeffrey De Fauw, Lucas Smaira, Sander Dieleman, Andrew Zisserman:
Self-Supervised MultiModal Versatile Networks. NeurIPS 2020 - [i18]Pauline Luc, Aidan Clark, Sander Dieleman, Diego de Las Casas, Yotam Doron, Albin Cassirer, Karen Simonyan:
Transformation-based Adversarial Video Prediction on Large-Scale Data. CoRR abs/2003.04035 (2020) - [i17]Jeff Donahue, Sander Dieleman, Mikolaj Binkowski, Erich Elsen, Karen Simonyan:
End-to-End Adversarial Text-to-Speech. CoRR abs/2006.03575 (2020) - [i16]Jean-Baptiste Alayrac, Adrià Recasens, Rosalia Schneider, Relja Arandjelovic, Jason Ramapuram, Jeffrey De Fauw, Lucas Smaira, Sander Dieleman, Andrew Zisserman:
Self-Supervised MultiModal Versatile Networks. CoRR abs/2006.16228 (2020) - [i15]Jamie Hayes, Krishnamurthy Dvijotham, Yutian Chen, Sander Dieleman, Pushmeet Kohli, Norman Casagrande:
Towards transformation-resilient provenance detection of digital media. CoRR abs/2011.07355 (2020)
2010 – 2019
- 2019
- [c20]Curtis Hawthorne, Andriy Stasyuk, Adam Roberts, Ian Simon, Cheng-Zhi Anna Huang, Sander Dieleman, Erich Elsen, Jesse H. Engel, Douglas Eck:
Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Dataset. ICLR 2019 - [c19]Chris Donahue, Ian Simon, Sander Dieleman:
Piano Genie. IUI 2019: 160-164 - [i14]Jeffrey De Fauw, Sander Dieleman, Karen Simonyan:
Hierarchical Autoregressive Image Models with Auxiliary Decoders. CoRR abs/1903.04933 (2019) - [i13]Mikolaj Binkowski, Jeff Donahue, Sander Dieleman, Aidan Clark, Erich Elsen, Norman Casagrande, Luis C. Cobo, Karen Simonyan:
High Fidelity Speech Synthesis with Adversarial Networks. CoRR abs/1909.11646 (2019) - 2018
- [j3]Lionel Pigou, Aäron van den Oord, Sander Dieleman, Mieke Van Herreweghe, Joni Dambre:
Beyond Temporal Pooling: Recurrence and Temporal Convolutions for Gesture Recognition in Video. Int. J. Comput. Vis. 126(2-4): 430-439 (2018) - [c18]Nal Kalchbrenner, Erich Elsen, Karen Simonyan, Seb Noury, Norman Casagrande, Edward Lockhart, Florian Stimberg, Aäron van den Oord, Sander Dieleman, Koray Kavukcuoglu:
Efficient Neural Audio Synthesis. ICML 2018: 2415-2424 - [c17]Aäron van den Oord, Yazhe Li, Igor Babuschkin, Karen Simonyan, Oriol Vinyals, Koray Kavukcuoglu, George van den Driessche, Edward Lockhart, Luis C. Cobo, Florian Stimberg, Norman Casagrande, Dominik Grewe, Seb Noury, Sander Dieleman, Erich Elsen, Nal Kalchbrenner, Heiga Zen, Alex Graves, Helen King, Tom Walters, Dan Belov, Demis Hassabis:
Parallel WaveNet: Fast High-Fidelity Speech Synthesis. ICML 2018: 3915-3923 - [c16]Sander Dieleman, Aäron van den Oord, Karen Simonyan:
The challenge of realistic music generation: modelling raw audio at scale. NeurIPS 2018: 8000-8010 - [c15]Balázs Hidasi, Alexandros Karatzoglou, Oren Sar Shalom, Bracha Shapira, Domonkos Tikk, Flavian Vasile, Sander Dieleman:
DLRS 2018: third workshop on deep learning for recommender systems. RecSys 2018: 512-513 - [e2]Balázs Hidasi, Alexandros Karatzoglou, Oren Sar Shalom, Bracha Shapira, Domonkos Tikk, Flavian Vasile, Sander Dieleman:
Proceedings of the 3rd Workshop on Deep Learning for Recommender Systems, DLRS@RecSys 2018, Vancouver, BC, Canada, October 6, 2018. ACM 2018, ISBN 978-1-4503-6617-5 [contents] - [i12]Nal Kalchbrenner, Erich Elsen, Karen Simonyan, Seb Noury, Norman Casagrande, Edward Lockhart, Florian Stimberg, Aäron van den Oord, Sander Dieleman, Koray Kavukcuoglu:
Efficient Neural Audio Synthesis. CoRR abs/1802.08435 (2018) - [i11]Sander Dieleman, Aäron van den Oord, Karen Simonyan:
The challenge of realistic music generation: modelling raw audio at scale. CoRR abs/1806.10474 (2018) - [i10]Sageev Oore, Ian Simon, Sander Dieleman, Douglas Eck, Karen Simonyan:
This Time with Feeling: Learning Expressive Musical Performance. CoRR abs/1808.03715 (2018) - [i9]Chris Donahue, Ian Simon, Sander Dieleman:
Piano Genie. CoRR abs/1810.05246 (2018) - [i8]Curtis Hawthorne, Andriy Stasyuk, Adam Roberts, Ian Simon, Cheng-Zhi Anna Huang, Sander Dieleman, Erich Elsen, Jesse H. Engel, Douglas Eck:
Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Dataset. CoRR abs/1810.12247 (2018) - 2017
- [c14]Jesse H. Engel, Cinjon Resnick, Adam Roberts, Sander Dieleman, Mohammad Norouzi, Douglas Eck, Karen Simonyan:
Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders. ICML 2017: 1068-1077 - [c13]Balázs Hidasi, Alexandros Karatzoglou, Oren Sar Shalom, Sander Dieleman, Bracha Shapira, Domonkos Tikk:
DLRS 2017: Second Workshop on Deep Learning for Recommender Systems. RecSys 2017: 370-371 - [e1]Balázs Hidasi, Alexandros Karatzoglou, Oren Sar Shalom, Sander Dieleman, Bracha Shapira, Domonkos Tikk:
Proceedings of the 2nd Workshop on Deep Learning for Recommender Systems, DLRS@RecSys 2017, Como, Italy, August 27, 2017. ACM 2017, ISBN 978-1-4503-5353-3 [contents] - [i7]Jesse H. Engel, Cinjon Resnick, Adam Roberts, Sander Dieleman, Douglas Eck, Karen Simonyan, Mohammad Norouzi:
Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders. CoRR abs/1704.01279 (2017) - [i6]Aäron van den Oord, Yazhe Li, Igor Babuschkin, Karen Simonyan, Oriol Vinyals, Koray Kavukcuoglu, George van den Driessche, Edward Lockhart, Luis C. Cobo, Florian Stimberg, Norman Casagrande, Dominik Grewe, Seb Noury, Sander Dieleman, Erich Elsen, Nal Kalchbrenner, Heiga Zen, Alex Graves, Helen King, Tom Walters, Dan Belov, Demis Hassabis:
Parallel WaveNet: Fast High-Fidelity Speech Synthesis. CoRR abs/1711.10433 (2017) - 2016
- [j2]David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Vedavyas Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy P. Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, Demis Hassabis:
Mastering the game of Go with deep neural networks and tree search. Nat. 529(7587): 484-489 (2016) - [c12]Jonas Degrave, Sander Dieleman, Joni Dambre, Francis Wyffels:
Spatial Chirp-Z Transformer Networks. ESANN 2016 - [c11]Sander Dieleman, Jeffrey De Fauw, Koray Kavukcuoglu:
Exploiting Cyclic Symmetry in Convolutional Neural Networks. ICML 2016: 1889-1898 - [c10]Sander Dieleman:
Keynote: Deep learning for audio-based music recommendation. DLRS@RecSys 2016: 1 - [c9]Aäron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew W. Senior, Koray Kavukcuoglu:
WaveNet: A Generative Model for Raw Audio. SSW 2016: 125 - [i5]Sander Dieleman, Jeffrey De Fauw, Koray Kavukcuoglu:
Exploiting Cyclic Symmetry in Convolutional Neural Networks. CoRR abs/1602.02660 (2016) - [i4]Rami Al-Rfou, Guillaume Alain, Amjad Almahairi, Christof Angermüller, Dzmitry Bahdanau, Nicolas Ballas, Frédéric Bastien, Justin Bayer, Anatoly Belikov, Alexander Belopolsky, Yoshua Bengio, Arnaud Bergeron, James Bergstra, Valentin Bisson, Josh Bleecher Snyder, Nicolas Bouchard, Nicolas Boulanger-Lewandowski, Xavier Bouthillier, Alexandre de Brébisson, Olivier Breuleux, Pierre Luc Carrier, Kyunghyun Cho, Jan Chorowski, Paul F. Christiano, Tim Cooijmans, Marc-Alexandre Côté, Myriam Côté, Aaron C. Courville, Yann N. Dauphin, Olivier Delalleau, Julien Demouth, Guillaume Desjardins, Sander Dieleman, Laurent Dinh, Melanie Ducoffe, Vincent Dumoulin, Samira Ebrahimi Kahou, Dumitru Erhan, Ziye Fan, Orhan Firat, Mathieu Germain, Xavier Glorot, Ian J. Goodfellow, Matthew Graham, Çaglar Gülçehre, Philippe Hamel, Iban Harlouchet, Jean-Philippe Heng, Balázs Hidasi, Sina Honari, Arjun Jain, Sébastien Jean, Kai Jia, Mikhail Korobov, Vivek Kulkarni, Alex Lamb, Pascal Lamblin, Eric Larsen, César Laurent, Sean Lee, Simon Lefrançois, Simon Lemieux, Nicholas Léonard, Zhouhan Lin, Jesse A. Livezey, Cory Lorenz, Jeremiah Lowin, Qianli Ma, Pierre-Antoine Manzagol, Olivier Mastropietro, Robert McGibbon, Roland Memisevic, Bart van Merriënboer, Vincent Michalski, Mehdi Mirza, Alberto Orlandi, Christopher Joseph Pal, Razvan Pascanu, Mohammad Pezeshki, Colin Raffel, Daniel Renshaw, Matthew Rocklin, Adriana Romero, Markus Roth, Peter Sadowski, John Salvatier, François Savard, Jan Schlüter, John Schulman, Gabriel Schwartz, Iulian Vlad Serban, Dmitriy Serdyuk, Samira Shabanian, Étienne Simon, Sigurd Spieckermann, S. Ramana Subramanyam, Jakub Sygnowski, Jérémie Tanguay, Gijs van Tulder, Joseph P. Turian, Sebastian Urban, Pascal Vincent, Francesco Visin, Harm de Vries, David Warde-Farley, Dustin J. Webb, Matthew Willson, Kelvin Xu, Lijun Xue, Li Yao, Saizheng Zhang, Ying Zhang:
Theano: A Python framework for fast computation of mathematical expressions. CoRR abs/1605.02688 (2016) - [i3]Aäron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew W. Senior, Koray Kavukcuoglu:
WaveNet: A Generative Model for Raw Audio. CoRR abs/1609.03499 (2016) - 2015
- [i2]Sander Dieleman, Kyle W. Willett, Joni Dambre:
Rotation-invariant convolutional neural networks for galaxy morphology prediction. CoRR abs/1503.07077 (2015) - [i1]Lionel Pigou, Aäron van den Oord, Sander Dieleman, Mieke Van Herreweghe, Joni Dambre:
Beyond Temporal Pooling: Recurrence and Temporal Convolutions for Gesture Recognition in Video. CoRR abs/1506.01911 (2015) - 2014
- [c8]Lionel Pigou, Sander Dieleman, Pieter-Jan Kindermans, Benjamin Schrauwen:
Sign Language Recognition Using Convolutional Neural Networks. ECCV Workshops (1) 2014: 572-578 - [c7]Sander Dieleman, Benjamin Schrauwen:
End-to-end learning for music audio. ICASSP 2014: 6964-6968 - [c6]Aäron van den Oord, Sander Dieleman, Benjamin Schrauwen:
Transfer Learning by Supervised Pre-training for Audio-based Music Classification. ISMIR 2014: 29-34 - 2013
- [c5]Ken Caluwaerts, Francis Wyffels, Sander Dieleman, Benjamin Schrauwen:
The spectral radius remains a valid indicator of the Echo state property for large reservoirs. IJCNN 2013: 1-6 - [c4]Sander Dieleman, Benjamin Schrauwen:
Multiscale Approaches To Music Audio Feature Learning. ISMIR 2013: 3-8 - [c3]Aäron van den Oord, Sander Dieleman, Benjamin Schrauwen:
Deep content-based music recommendation. NIPS 2013: 2643-2651 - 2012
- [j1]David Verstraeten, Benjamin Schrauwen, Sander Dieleman, Philemon Brakel, Pieter Buteneers, Dejan Pecevski:
Oger: modular learning architectures for large-scale sequential processing. J. Mach. Learn. Res. 13: 2995-2998 (2012) - [c2]Philemon Brakel, Sander Dieleman, Benjamin Schrauwen:
Training Restricted Boltzmann Machines with Multi-tempering: Harnessing Parallelization. ICANN (2) 2012: 92-99 - 2011
- [c1]Sander Dieleman, Philemon Brakel, Benjamin Schrauwen:
Audio-based Music Classification with a Pretrained Convolutional Network. ISMIR 2011: 669-674
Coauthor Index
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