Recently, ChatGPT, along with DALL-E-2 and Codex, has been gaining significant attention
from society. As a result, many individuals have become interested in related resources and …

Normalizing flows provide a general mechanism for defining expressive probability
distributions, only requiring the specification of a (usually simple) base distribution and a …

Deep generative models are a class of techniques that train deep neural networks to model
the distribution of training samples. Research has fragmented into various interconnected …

Normalizing Flows are generative models which produce tractable distributions where both
sampling and density evaluation can be efficient and exact. The goal of this survey article is …

A normalizing flow models a complex probability density as an invertible transformation of a
simple base density. Flows based on either coupling or autoregressive transforms both offer …

Abstract Recently, text-to-speech (TTS) models such as FastSpeech and ParaNet have been
proposed to generate mel-spectrograms from text in parallel. Despite the advantage, the …

Detecting out-of-distribution (OOD) data is crucial for robust machine learning systems.
Normalizing flows are flexible deep generative models that often surprisingly fail to …

Normalizing Flows (NFs) are able to model complicated distributions p (y) with strong inter-
dimensional correlations and high multimodality by transforming a simple base density p (z) …

The Hamiltonian formalism plays a central role in classical and quantum physics.
Hamiltonians are the main tool for modelling the continuous time evolution of systems with …

Anomaly detection is a widely studied task for a broad variety of data types; among them,
multiple time series appear frequently in applications, including for example, power grids …