65 citations · 170 across the 9 of their papers we have counts for
9 papers
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…
A Hierarchical Bayesian Model for Size Recommendation in Fashion
Romain Guigourès, Yuen King Ho, Evgenii Koriagin +3
We introduce a hierarchical Bayesian approach to tackle the challenging problem of size recommendation in e-commerce fashion. Our approach jointly models a size purchased by a cust…
ProSper -- A Python Library for Probabilistic Sparse Coding with Non-Standard Priors and Superpositions
Georgios Exarchakis, Jörg Bornschein, Abdul-Saboor Sheikh +4
ProSper is a python library containing probabilistic algorithms to learn dictionaries. Given a set of data points, the implemented algorithms seek to learn the elementary component…
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…
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…
Stochastic Maximum Likelihood Optimization via Hypernetworks
Abdul-Saboor Sheikh, Kashif Rasul, Andreas Merentitis +1
This work explores maximum likelihood optimization of neural networks through hypernetworks. A hypernetwork initializes the weights of another network, which in turn can be employe…
Neural Simpletrons - Minimalistic Directed Generative Networks for Learning with Few Labels
Dennis Forster, Abdul-Saboor Sheikh, Jörg Lücke
Classifiers for the semi-supervised setting often combine strong supervised models with additional learning objectives to make use of unlabeled data. This results in powerful thoug…
A Truncated EM Approach for Spike-and-Slab Sparse Coding
Abdul-Saboor Sheikh, Jacquelyn A. Shelton, Jörg Lücke
We study inference and learning based on a sparse coding model with `spike-and-slab' prior. As in standard sparse coding, the model used assumes independent latent sources that lin…
Closed-form EM for Sparse Coding and its Application to Source Separation
Jörg Lücke, Abdul-Saboor Sheikh
We define and discuss the first sparse coding algorithm based on closed-form EM updates and continuous latent variables. The underlying generative model consists of a standard `spi…