Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting

1 code implementation • 28 Jan 2021 • Kashif Rasul, Calvin Seward, Ingmar Schuster, Roland Vollgraf

In this work, we propose \texttt{TimeGrad}, an autoregressive model for multivariate probabilistic time series forecasting which samples from the data distribution at each time step by estimating its gradient.

Multivariate Time Series Forecasting Probabilistic Time Series Forecasting +1

Disentangling Multiple Conditional Inputs in GANs

2 code implementations • 20 Jun 2018 • Gökhan Yildirim, Calvin Seward, Urs Bergmann

In this paper, we propose a method that disentangles the effects of multiple input conditions in Generative Adversarial Networks (GANs).

First Order Generative Adversarial Networks

1 code implementation • ICML 2018 • Calvin Seward, Thomas Unterthiner, Urs Bergmann, Nikolay Jetchev, Sepp Hochreiter

To formally describe an optimal update direction, we introduce a theoretical framework which allows the derivation of requirements on both the divergence and corresponding method for determining an update direction, with these requirements guaranteeing unbiased mini-batch updates in the direction of steepest descent.

Image Generation Text Generation

GANosaic: Mosaic Creation with Generative Texture Manifolds

no code implementations • 1 Dec 2017 • Nikolay Jetchev, Urs Bergmann, Calvin Seward

This paper presents a novel framework for generating texture mosaics with convolutional neural networks.

MORPH Texture Synthesis

Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields

1 code implementation • ICLR 2018 • Thomas Unterthiner, Bernhard Nessler, Calvin Seward, Günter Klambauer, Martin Heusel, Hubert Ramsauer, Sepp Hochreiter

We prove that Coulomb GANs possess only one Nash equilibrium which is optimal in the sense that the model distribution equals the target distribution.