Audio-based Music Classification with a Pretrained Convolutional Network. - Sander Dieleman; Philemon Brakel; Benjamin Schrauwen (ISMIR, 2011)

Training Restricted Boltzmann Machines with Multi-tempering: Harnessing Parallelization. - Philemon Brakel; Sander Dieleman; Benjamin Schrauwen (ICANN (2), 2012)

Oger: modular learning architectures for large-scale sequential processing. - David Verstraeten; Benjamin Schrauwen; Sander Dieleman; Philemon Brakel; Pieter Buteneers; Dejan Pecevski (J. Mach. Learn. Res., 2012)

The spectral radius remains a valid indicator of the Echo state property for large reservoirs. - Ken Caluwaerts; Francis Wyffels; Sander Dieleman; Benjamin Schrauwen (IJCNN, 2013)

Multiscale Approaches To Music Audio Feature Learning. - Sander Dieleman; Benjamin Schrauwen (ISMIR, 2013)

Deep content-based music recommendation. - Aäron van den Oord; Sander Dieleman; Benjamin Schrauwen (NIPS, 2013)

Sign Language Recognition Using Convolutional Neural Networks. - Lionel Pigou; Sander Dieleman; Pieter-Jan Kindermans; Benjamin Schrauwen (ECCV Workshops (1), 2014)

End-to-end learning for music audio. - Sander Dieleman; Benjamin Schrauwen (ICASSP, 2014)

Transfer Learning by Supervised Pre-training for Audio-based Music Classification. - Aäron van den Oord; Sander Dieleman; Benjamin Schrauwen (ISMIR, 2014)

Rotation-invariant convolutional neural networks for galaxy morphology prediction. - Sander Dieleman; Kyle W. Willett; Joni Dambre (CoRR, 2015)

Spatial Chirp-Z Transformer Networks. - Jonas Degrave; Sander Dieleman; Joni Dambre; Francis Wyffels (ESANN, 2016)

Exploiting Cyclic Symmetry in Convolutional Neural Networks. - Sander Dieleman; Jeffrey De Fauw; Koray Kavukcuoglu (ICML, 2016)

Keynote: Deep learning for audio-based music recommendation. - Sander Dieleman (DLRS@RecSys, 2016)

WaveNet: A Generative Model for Raw Audio. - Aäron van den Oord; Sander Dieleman; Heiga Zen; Karen Simonyan; Oriol Vinyals; Alex Graves; Nal Kalchbrenner; Andrew W. Senior; Koray Kavukcuoglu (SSW, 2016)

Theano: A Python framework for fast computation of mathematical expressions. - 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; et al. (CoRR, 2016)

Mastering the game of Go with deep neural networks and tree search. - David Silver; Aja Huang; Chris J. Maddison; Arthur Guez; Laurent Sifre; George van den Driessche 002; Julian Schrittwieser; Ioannis Antonoglou; Vedavyas Panneershelvam; Marc Lanctot; Sander Dieleman; Dominik Grewe; et al. (Nat., 2016)

Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders. - Jesse H. Engel; Cinjon Resnick; Adam Roberts; Sander Dieleman; Mohammad Norouzi 002; Douglas Eck; Karen Simonyan (ICML, 2017)

DLRS 2017: Second Workshop on Deep Learning for Recommender Systems. - Balázs Hidasi; Alexandros Karatzoglou; Oren Sar Shalom; Sander Dieleman; Bracha Shapira; Domonkos Tikk (RecSys, 2017)

Efficient Neural Audio Synthesis. - Nal Kalchbrenner; Erich Elsen; Karen Simonyan; Seb Noury; Norman Casagrande; Edward Lockhart; Florian Stimberg; Aäron van den Oord; Sander Dieleman; Koray Kavukcuoglu (ICML, 2018)

Parallel WaveNet: Fast High-Fidelity Speech Synthesis. - Aäron van den Oord; Yazhe Li; Igor Babuschkin; Karen Simonyan; Oriol Vinyals; Koray Kavukcuoglu; George van den Driessche 002; Edward Lockhart; Luis C. Cobo; Florian Stimberg; Norman Casagrande; Dominik Grewe; et al. (ICML, 2018)

The challenge of realistic music generation: modelling raw audio at scale. - Sander Dieleman; Aäron van den Oord; Karen Simonyan (NeurIPS, 2018)

DLRS 2018: third workshop on deep learning for recommender systems. - Balázs Hidasi; Alexandros Karatzoglou; Oren Sar Shalom; Bracha Shapira; Domonkos Tikk; Flavian Vasile; Sander Dieleman (RecSys, 2018)

Beyond Temporal Pooling: Recurrence and Temporal Convolutions for Gesture Recognition in Video. - Lionel Pigou; Aäron van den Oord; Sander Dieleman; Mieke Van Herreweghe; Joni Dambre (Int. J. Comput. Vis., 2018)

Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Dataset. - Curtis Hawthorne; Andriy Stasyuk; Adam Roberts; Ian Simon; Cheng-Zhi Anna Huang; Sander Dieleman; Erich Elsen; Jesse H. Engel; Douglas Eck (ICLR, 2019)

Piano Genie. - Chris Donahue; Ian Simon; Sander Dieleman (IUI, 2019)

Hierarchical Autoregressive Image Models with Auxiliary Decoders. - Jeffrey De Fauw; Sander Dieleman; Karen Simonyan (CoRR, 2019)

High Fidelity Speech Synthesis with Adversarial Networks. - Mikolaj Binkowski; Jeff Donahue; Sander Dieleman; Aidan Clark; Erich Elsen; Norman Casagrande; Luis C. Cobo; Karen Simonyan (ICLR, 2020)

Self-Supervised MultiModal Versatile Networks. - Jean-Baptiste Alayrac; Adrià Recasens; Rosalia Schneider; Relja Arandjelovic; Jason Ramapuram; Jeffrey De Fauw; Lucas Smaira; Sander Dieleman; Andrew Zisserman (NeurIPS, 2020)

Transformation-based Adversarial Video Prediction on Large-Scale Data. - Pauline Luc; Aidan Clark; Sander Dieleman; Diego de Las Casas; Yotam Doron; Albin Cassirer; Karen Simonyan (CoRR, 2020)

Towards transformation-resilient provenance detection of digital media. - Jamie Hayes; Krishnamurthy Dvijotham; Yutian Chen; Sander Dieleman; Pushmeet Kohli; Norman Casagrande (CoRR, 2020)

This time with feeling: learning expressive musical performance. - Sageev Oore; Ian Simon; Sander Dieleman; Douglas Eck; Karen Simonyan (Neural Comput. Appl., 2020)

End-to-end Adversarial Text-to-Speech. - Jeff Donahue; Sander Dieleman; Mikolaj Binkowski; Erich Elsen; Karen Simonyan (ICLR, 2021)

Generating images with sparse representations. - Charlie Nash; Jacob Menick; Sander Dieleman; Peter W. Battaglia (ICML, 2021)

A Deep Learning Approach for Characterizing Major Galaxy Mergers. - 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; et al. (CoRR, 2021)

Variable-rate discrete representation learning. - Sander Dieleman; Charlie Nash; Jesse H. Engel; Karen Simonyan (CoRR, 2021)

Towards Learning Universal Audio Representations. - Luyu Wang; Pauline Luc; Yan Wu 010; Adrià Recasens; Lucas Smaira; Andrew Brock; Andrew Jaegle; Jean-Baptiste Alayrac; Sander Dieleman; João Carreira 001; Aäron van den Oord (ICASSP, 2022)

General-purpose, long-context autoregressive modeling with Perceiver AR. - 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; et al. (ICML, 2022)

Categorical SDEs with Simplex Diffusion. - Pierre H. Richemond; Sander Dieleman; Arnaud Doucet (CoRR, 2022)

Self-conditioned Embedding Diffusion for Text Generation. - Robin Strudel; Corentin Tallec; Florent Altché; Yilun Du; Yaroslav Ganin; Arthur Mensch; Will Grathwohl; Nikolay Savinov; Sander Dieleman; Laurent Sifre; Rémi Leblond (CoRR, 2022)

Continuous diffusion for categorical data. - 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; et al. (CoRR, 2022)

Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMC. - Yilun Du; Conor Durkan; Robin Strudel; Joshua B. Tenenbaum; Sander Dieleman; Rob Fergus; Jascha Sohl-Dickstein; Arnaud Doucet; Will Sussman Grathwohl (ICML, 2023)

The NeurIPS 2023 Machine Learning for Audio Workshop: Affective Audio Benchmarks and Novel Data. - Alice Baird; Rachel Manzelli; Panagiotis Tzirakis; Chris Gagne 001; Haoqi Li; Sadie Allen; Sander Dieleman; Brian Kulis; Shrikanth S. Narayanan; Alan Cowen (CoRR, 2024)

Imagen 3. - 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; et al. (CoRR, 2024)