1 code implementation • 8 Jul 2021 • Nikolay Jetchev, Gökhan Yildirim, Christian Bracher, Roland Vollgraf
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 generation.
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
1 code implementation • COLING 2020 • Kishaloy Halder, Alan Akbik, Josip Krapac, Roland Vollgraf
State-of-the-art approaches for text classification leverage a transformer architecture with a linear layer on top that outputs a class distribution for a given prediction problem.
1 code implementation • 9 Jun 2020 • 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 to (1) mutual contacts between pairs of individuals across time across various channels (e. g., Bluetooth contact traces), as well as (2) test outcomes at given times for infection, exposure and immunity tests.
1 code implementation • ICLR 2021 • Kashif Rasul, Abdul-Saboor Sheikh, Ingmar Schuster, Urs Bergmann, Roland Vollgraf
In this work we model the multivariate temporal dynamics of time series via an autoregressive deep learning model, where the data distribution is represented by a conditioned normalizing flow.
no code implementations • 6 Sep 2019 • Kashif Rasul, Ingmar Schuster, Roland Vollgraf, Urs Bergmann
We present a generative model that is defined on finite sets of exchangeable, potentially high dimensional, data.
no code implementations • 23 Aug 2019 • Gökhan Yildirim, Nikolay Jetchev, Roland Vollgraf, Urs Bergmann
Visualizing an outfit is an essential part of shopping for clothes.
Conditional Image Generation Vocal Bursts Intensity Prediction
3 code implementations • 23 Jul 2019 • Abdul-Saboor Sheikh, Romain Guigoures, Evgenii Koriagin, Yuen King Ho, Reza Shirvany, Roland Vollgraf, Urs Bergmann
To alleviate this problem, we propose a deep learning based content-collaborative methodology for personalized size and fit recommendation.
1 code implementation • 22 Jun 2019 • Roland Vollgraf
The architecture is based on a gated recurrent network which is iteratively applied to all entities individually and at the same time syncs with the progression of the whole population.
no code implementations • 10 Feb 2019 • Andreas Merentitis, Kashif Rasul, Roland Vollgraf, Abdul-Saboor Sheikh, Urs Bergmann
This helps the bandit framework to select the best agents early, since these rewards are smoother and less sparse than the environment reward.
no code implementations • 2 Jul 2018 • 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 items.
no code implementations • 2 Mar 2018 • Duncan Blythe, Alan Akbik, Roland Vollgraf
Neural language models (LMs) are typically trained using only lexical features, such as surface forms of words.
37 code implementations • 25 Aug 2017 • 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.
no code implementations • 24 Aug 2017 • 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.
7 code implementations • ICML 2017 • Urs Bergmann, Nikolay Jetchev, Roland Vollgraf
Second, we show that the image generation with PSGANs has properties of a texture manifold: we can smoothly interpolate between samples in the structured noise space and generate novel samples, which lie perceptually between the textures of the original dataset.
3 code implementations • 24 Nov 2016 • 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.
no code implementations • 8 Sep 2016 • Christian Bracher, Sebastian Heinz, Roland Vollgraf
Interpretation of the metric of these spaces is straightforward: The product of Fashion DNA and customer style vectors yields the forecast purchase likelihood for the customer-item pair, while the angle between Fashion DNA vectors is a measure of item similarity.