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Found 17 papers, 10 papers with code
Grid Partitioned Attention: Efficient TransformerApproximation with Inductive Bias for High Resolution Detail Generation
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.
Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting
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
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Task-Aware Representation of Sentences for Generic Text Classification
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.
CRISP: A Probabilistic Model for Individual-Level COVID-19 Infection Risk Estimation Based on Contact Data
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.
Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows
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.
Set Flow: A Permutation Invariant Normalizing Flow
We present a generative model that is defined on finite sets of exchangeable, potentially high dimensional, data.
Generating High-Resolution Fashion Model Images Wearing Custom Outfits
Visualizing an outfit is an essential part of shopping for clothes.
Conditional Image Generation
Vocal Bursts Intensity Prediction
A Deep Learning System for Predicting Size and Fit in Fashion E-Commerce
To alleviate this problem, we propose a deep learning based content-collaborative methodology for personalized size and fit recommendation.
Learning Set-equivariant Functions with SWARM Mappings
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.
A Bandit Framework for Optimal Selection of Reinforcement Learning Agents
This helps the bandit framework to select the best agents early, since these rewards are smoother and less sparse than the environment reward.
Studio2Shop: from studio photo shoots to fashion articles
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.
Syntax-Aware Language Modeling with Recurrent Neural Networks
Neural language models (LMs) are typically trained using only lexical features, such as surface forms of words.
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
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.
An LSTM-Based Dynamic Customer Model for Fashion Recommendation
Online fashion sales present a challenging use case for personalized recommendation: Stores offer a huge variety of items in multiple sizes.
Learning Texture Manifolds with the Periodic Spatial GAN
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.
Texture Synthesis with Spatial Generative Adversarial Networks
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.
Fashion DNA: Merging Content and Sales Data for Recommendation and Article Mapping
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.
