Search Results for author: Sander Dieleman

Found 24 papers, 13 papers with code

Variable-rate discrete representation learning

no code implementations10 Mar 2021 Sander Dieleman, Charlie Nash, Jesse Engel, Karen Simonyan

Semantically meaningful information content in perceptual signals is usually unevenly distributed.

Representation Learning

Generating Images with Sparse Representations

1 code implementation5 Mar 2021 Charlie Nash, Jacob Menick, Sander Dieleman, Peter W. Battaglia

The high dimensionality of images presents architecture and sampling-efficiency challenges for likelihood-based generative models.

Colorization Image Compression +1

Towards transformation-resilient provenance detection of digital media

no code implementations14 Nov 2020 Jamie Hayes, Krishnamurthy, Dvijotham, Yutian Chen, Sander Dieleman, Pushmeet Kohli, Norman Casagrande

In this paper, we introduce ReSWAT (Resilient Signal Watermarking via Adversarial Training), a framework for learning transformation-resilient watermark detectors that are able to detect a watermark even after a signal has been through several post-processing transformations.

Self-Supervised MultiModal Versatile Networks

1 code implementation NeurIPS 2020 Jean-Baptiste Alayrac, Adrià Recasens, Rosalia Schneider, Relja Arandjelović, Jason Ramapuram, Jeffrey De Fauw, Lucas Smaira, Sander Dieleman, Andrew Zisserman

In particular, we explore how best to combine the modalities, such that fine-grained representations of the visual and audio modalities can be maintained, whilst also integrating text into a common embedding.

Action Recognition In Videos Audio Classification +2

End-to-End Adversarial Text-to-Speech

2 code implementations ICLR 2021 Jeff Donahue, Sander Dieleman, Mikołaj Bińkowski, Erich Elsen, Karen Simonyan

Modern text-to-speech synthesis pipelines typically involve multiple processing stages, each of which is designed or learnt independently from the rest.

Adversarial Text Dynamic Time Warping +2

Transformation-based Adversarial Video Prediction on Large-Scale Data

no code implementations9 Mar 2020 Pauline Luc, Aidan Clark, Sander Dieleman, Diego de Las Casas, Yotam Doron, Albin Cassirer, Karen Simonyan

Recent breakthroughs in adversarial generative modeling have led to models capable of producing video samples of high quality, even on large and complex datasets of real-world video.

Video Generation Video Prediction

Provenance detection through learning transformation-resilient watermarking

no code implementations25 Sep 2019 Jamie Hayes, Krishnamurthy Dvijotham, Yutian Chen, Sander Dieleman, Pushmeet Kohli, Norman Casagrande

In this paper, we introduce ReSWAT (Resilient Signal Watermarking via Adversarial Training), a framework for learning transformation-resilient watermark detectors that are able to detect a watermark even after a signal has been through several post-processing transformations.

High Fidelity Speech Synthesis with Adversarial Networks

3 code implementations ICLR 2020 Mikołaj Bińkowski, Jeff Donahue, Sander Dieleman, Aidan Clark, Erich Elsen, Norman Casagrande, Luis C. Cobo, Karen Simonyan

However, their application in the audio domain has received limited attention, and autoregressive models, such as WaveNet, remain the state of the art in generative modelling of audio signals such as human speech.

Speech Synthesis

Hierarchical Autoregressive Image Models with Auxiliary Decoders

no code implementations6 Mar 2019 Jeffrey De Fauw, Sander Dieleman, Karen Simonyan

We show that autoregressive models conditioned on these representations can produce high-fidelity reconstructions of images, and that we can train autoregressive priors on these representations that produce samples with large-scale coherence.

Piano Genie

no code implementations11 Oct 2018 Chris Donahue, Ian Simon, Sander Dieleman

We present Piano Genie, an intelligent controller which allows non-musicians to improvise on the piano.

This Time with Feeling: Learning Expressive Musical Performance

4 code implementations10 Aug 2018 Sageev Oore, Ian Simon, Sander Dieleman, Douglas Eck, Karen Simonyan

Music generation has generally been focused on either creating scores or interpreting them.

Music Generation

The challenge of realistic music generation: modelling raw audio at scale

no code implementations NeurIPS 2018 Sander Dieleman, Aäron van den Oord, Karen Simonyan

It has been shown that autoregressive models excel at generating raw audio waveforms of speech, but when applied to music, we find them biased towards capturing local signal structure at the expense of modelling long-range correlations.

Music Generation

Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders

5 code implementations ICML 2017 Jesse Engel, Cinjon Resnick, Adam Roberts, Sander Dieleman, Douglas Eck, Karen Simonyan, Mohammad Norouzi

Generative models in vision have seen rapid progress due to algorithmic improvements and the availability of high-quality image datasets.

Theano: A Python framework for fast computation of mathematical expressions

1 code implementation9 May 2016 The Theano Development Team, Rami Al-Rfou, Guillaume Alain, Amjad Almahairi, Christof Angermueller, 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 Christiano, Tim Cooijmans, Marc-Alexandre Côté, Myriam Côté, Aaron Courville, Yann N. Dauphin, Olivier Delalleau, Julien Demouth, Guillaume Desjardins, Sander Dieleman, Laurent Dinh, Mélanie Ducoffe, Vincent Dumoulin, Samira Ebrahimi Kahou, Dumitru Erhan, Ziye Fan, Orhan Firat, Mathieu Germain, Xavier Glorot, Ian Goodfellow, Matt Graham, Caglar Gulcehre, 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 Lefrancois, Simon Lemieux, Nicholas Léonard, Zhouhan Lin, Jesse A. Livezey, Cory Lorenz, Jeremiah Lowin, Qianli Ma, Pierre-Antoine Manzagol, Olivier Mastropietro, Robert T. McGibbon, Roland Memisevic, Bart van Merriënboer, Vincent Michalski, Mehdi Mirza, Alberto Orlandi, Christopher 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 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

Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially in the machine learning community - and has shown steady performance improvements.

Dimensionality Reduction General Classification

Exploiting Cyclic Symmetry in Convolutional Neural Networks

no code implementations8 Feb 2016 Sander Dieleman, Jeffrey De Fauw, Koray Kavukcuoglu

We evaluate the effect of these architectural modifications on three datasets which exhibit rotational symmetry and demonstrate improved performance with smaller models.

Data Augmentation Translation

Rotation-invariant convolutional neural networks for galaxy morphology prediction

2 code implementations24 Mar 2015 Sander Dieleman, Kyle W. Willett, Joni Dambre

Unfortunately, even this approach does not scale well enough to keep up with the increasing availability of galaxy images.

General Classification Morphological Analysis +1

Deep content-based music recommendation

no code implementations NeurIPS 2013 Aaron Van Den Oord, Sander Dieleman, Benjamin Schrauwen

We also show that recent advances in deep learning translate very well to the music recommendation setting, with deep convolutional neural networks significantly outperforming the traditional approach.

Collaborative Filtering Recommendation Systems

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