6.1k citations · 6.5k across the 16 of their papers we have counts for
16 papers
Grid Partitioned Attention: Efficient TransformerApproximation with Inductive Bias for High Resolution Detail Generation
Nikolay Jetchev, Gökhan Yildirim, Christian Bracher +1
Attention is a general reasoning mechanism than can flexibly deal with image information, but its memory requirements had made it so far impractical for high resolution image gener…
Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting
Kashif Rasul, Calvin Seward, Ingmar Schuster +1
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 ste…
CRISP: A Probabilistic Model for Individual-Level COVID-19 Infection Risk Estimation Based on Contact Data
Ralf Herbrich, Rajeev Rastogi, Roland Vollgraf
We present CRISP (COVID-19 Risk Score Prediction), a probabilistic graphical model for COVID-19 infection spread through a population based on the SEIR model where we assume access…
Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows
Kashif Rasul, Abdul-Saboor Sheikh, Ingmar Schuster +2
Time series forecasting is often fundamental to scientific and engineering problems and enables decision making. With ever increasing data set sizes, a trivial solution to scale up…
Set Flow: A Permutation Invariant Normalizing Flow
Kashif Rasul, Ingmar Schuster, Roland Vollgraf +1
We present a generative model that is defined on finite sets of exchangeable, potentially high dimensional, data. As the architecture is an extension of RealNVPs, it inherits all i…
Generating High-Resolution Fashion Model Images Wearing Custom Outfits
Gökhan Yildirim, Nikolay Jetchev, Roland Vollgraf +1
Visualizing an outfit is an essential part of shopping for clothes. Due to the combinatorial aspect of combining fashion articles, the available images are limited to a pre-determi…
A Deep Learning System for Predicting Size and Fit in Fashion E-Commerce
Abdul-Saboor Sheikh, Romain Guigoures, Evgenii Koriagin +4
Personalized size and fit recommendations bear crucial significance for any fashion e-commerce platform. Predicting the correct fit drives customer satisfaction and benefits the bu…
Learning Set-equivariant Functions with SWARM Mappings
Roland Vollgraf
In this work we propose a new neural network architecture that efficiently implements and learns general purpose set-equivariant functions. Such a function f maps a set of entities…
A Bandit Framework for Optimal Selection of Reinforcement Learning Agents
Andreas Merentitis, Kashif Rasul, Roland Vollgraf +2
Deep Reinforcement Learning has been shown to be very successful in complex games, e.g. Atari or Go. These games have clearly defined rules, and hence allow simulation. In many pra…
Studio2Shop: from studio photo shoots to fashion articles
Julia Lasserre, Katharina Rasch, Roland Vollgraf
Fashion is an increasingly important topic in computer vision, in particular the so-called street-to-shop task of matching street images with shop images containing similar fashion…
Syntax-Aware Language Modeling with Recurrent Neural Networks
Duncan Blythe, Alan Akbik, Roland Vollgraf
Neural language models (LMs) are typically trained using only lexical features, such as surface forms of words. In this paper, we argue this deprives the LM of crucial syntactic si…
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
Han Xiao, Kashif Rasul, Roland Vollgraf
We present Fashion-MNIST, a new dataset comprising of 28x28 grayscale images of 70,000 fashion products from 10 categories, with 7,000 images per category. The training set has 60,…
An LSTM-Based Dynamic Customer Model for Fashion Recommendation
Sebastian Heinz, Christian Bracher, Roland Vollgraf
Online fashion sales present a challenging use case for personalized recommendation: Stores offer a huge variety of items in multiple sizes. Small stocks, high return rates, season…
Learning Texture Manifolds with the Periodic Spatial GAN
Urs Bergmann, Nikolay Jetchev, Roland Vollgraf
This paper introduces a novel approach to texture synthesis based on generative adversarial networks (GAN) (Goodfellow et al., 2014). We extend the structure of the input noise dis…
Texture Synthesis with Spatial Generative Adversarial Networks
Nikolay Jetchev, Urs Bergmann, Roland Vollgraf
Generative adversarial networks (GANs) are a recent approach to train generative models of data, which have been shown to work particularly well on image data. In the current paper…
Fashion DNA: Merging Content and Sales Data for Recommendation and Article Mapping
Christian Bracher, Sebastian Heinz, Roland Vollgraf
We present a method to determine Fashion DNA, coordinate vectors locating fashion items in an abstract space. Our approach is based on a deep neural network architecture that inges…