Diffusion models have shown impressive results in text-to-image synthesis. Using massive
datasets of captioned images, diffusion models learn to generate raster images of highly …

Realistic music generation is a challenging task. When building generative models of music
that are learnt from data, typically high-level representations such as scores or MIDI are …

We present a Parallel Iterative Edit (PIE) model for the problem of local sequence
transduction arising in tasks like Grammatical error correction (GEC). Recent approaches …

Conventional neural autoregressive decoding commonly assumes a fixed left-to-right
generation order, which may be sub-optimal. In this work, we propose a novel decoding …

Recent advances in deep learning have shown impressive results in the domain of text-to-
speech. To this end, a deep neural network is usually trained using a corpus of several …

Canonical transformation plays a fundamental role in simplifying and solving classical
Hamiltonian systems. Intriguingly, it has a natural correspondence to normalizing flows with …

Convolutional neural networks (CNNs) with dilated filters such as the Wavenet or the
Temporal Convolutional Network (TCN) have shown good results in a variety of sequence …

Deep learning has become an area of interest in most scientific areas, including physical
sciences. Modern networks apply real-valued transformations on the data. Particularly …

Autoregressive neural networks, such as WaveNet, have opened up new avenues for
expressive audio synthesis. High-quality speech synthesis utilizes detailed linguistic …

Autoregressive models are widely used for tasks such as image and audio generation. The
sampling process of these models, however, does not allow interruptions and cannot adapt …