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Top Papers in Generative modelling

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On the Quantum versus Classical Learnability of Discrete Distributions

Here we study the comparative power of classical and quantum learners for generative modelling within the Probably Approximately Correct (PAC) framework. More specifically we consider the following ta

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A single $T$-gate makes distribution learning hard

The task of learning a probability distribution from samples is ubiquitous
across the natural sciences. The output distributions of local quantum circuits
form a particularly interesting class of dist

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Intrinsically Inverting Heat Equations for Multi-Scale Image Generation

Generative Modelling With Inverse Heat Dissipation

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TIGGER: Scalable Generative Modelling for Temporal Interaction Graphs

There has been a recent surge in learning generative models for graphs. While
impressive progress has been made on static graphs, work on generative modeling
of temporal graphs is at a nascent stage w

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Variational Autoencoders Without the Variation

Variational autoencdoers (VAE) are a popular approach to generative
modelling. However, exploiting the capabilities of VAEs in practice can be
difficult. Recent work on regularised and entropic autoen

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Lifelong Generative Modelling Using Dynamic Expansion Graph Model

Variational Autoencoders (VAEs) suffer from degenerated performance, when
learning several successive tasks. This is caused by catastrophic forgetting.
In order to address the knowledge loss, VAEs are

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Quantum versus Classical Generative Modelling in Finance

Finding a concrete use case for quantum computers in the near term is still an open question, with machine learning typically touted as one of the first fields which will be impacted by quantum techno

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A Data-driven Market Simulator for Small Data Environments

Neural network based data-driven market simulation unveils a new and flexible
way of modelling financial time series without imposing assumptions on the
underlying stochastic dynamics. Though in this

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Learning generative models over functional spaces

Spectral Diffusion Processes

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Random Forests for Density Estimation and Data Synthesis

Adversarial random forests for density estimation and generative modelling

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Neural Implicit Manifold Learning for Topology-Aware Generative Modelling

Natural data observed in $\mathbb{R}^n$ is often constrained to an
$m$-dimensional manifold $\mathcal{M}$, where $m < n$. Current generative
models represent this manifold by mapping an $m$-dimensiona

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Generative Modelling of BRDF Textures from Flash Images

We learn a latent space for easy capture, semantic editing, consistent
interpolation, and efficient reproduction of visual material appearance. When
users provide a photo of a stationary natural mater

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Protocols for Trainable and Differentiable Quantum Generative Modelling

We propose an approach for learning probability distributions as
differentiable quantum circuits (DQC) that enable efficient quantum generative
modelling (QGM) and synthetic data generation. Contrary

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Diffusion Priors In Variational Autoencoders

Among likelihood-based approaches for deep generative modelling, variational
autoencoders (VAEs) offer scalable amortized posterior inference and fast
sampling. However, VAEs are also more and more ou

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An Appendix for Lifelong Generative Modelling Using Dynamic Expansion Graph Model

Supplemental Material: Lifelong Generative Modelling Using Dynamic Expansion Graph Model

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Added to collectionDocument Analysis

Graph Neural Networks for Document Layout Generation

Graph-based Deep Generative Modelling for Document Layout Generation

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A Deep generative model capable of generating synthetic images of the human brain

Morphology-preserving Autoregressive 3D Generative Modelling of the Brain

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High Fidelity Speech Synthesis with Adversarial Networks

Generative adversarial networks have seen rapid development in recent years
and have led to remarkable improvements in generative modelling of images.
However, their application in the audio domain ha

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GAN-based generative modelling for dermatological applications -- comparative study

The lack of sufficiently large open medical databases is one of the biggest
challenges in AI-powered healthcare. Synthetic data created using Generative
Adversarial Networks (GANs) appears to be a goo

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Machine Learning

High Energy Physics - Experiment

High Energy Physics - Phenomenology

Data Analysis

Quantum Physics

Quantum-probabilistic Hamiltonian learning for generative modelling & anomaly detection

The Hamiltonian of an isolated quantum mechanical system determines its
dynamics and physical behaviour. This study investigates the possibility of
learning and utilising a system's Hamiltonian and it

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