Search Results for author: Abdul-Saboor Sheikh

Found 9 papers, 2 papers with code

Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows

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.

Decision Making Multivariate Time Series Forecasting +3

A Hierarchical Bayesian Model for Size Recommendation in Fashion

no code implementations2 Aug 2019 Romain Guigourès, Yuen King Ho, Evgenii Koriagin, Abdul-Saboor Sheikh, Urs Bergmann, Reza Shirvany

We introduce a hierarchical Bayesian approach to tackle the challenging problem of size recommendation in e-commerce fashion.

ProSper -- A Python Library for Probabilistic Sparse Coding with Non-Standard Priors and Superpositions

no code implementations1 Aug 2019 Georgios Exarchakis, Jörg Bornschein, Abdul-Saboor Sheikh, Zhenwen Dai, Marc Henniges, Jakob Drefs, Jörg Lücke

The library widens the scope of dictionary learning approaches beyond implementations of standard approaches such as ICA, NMF or standard L1 sparse coding.

Dictionary Learning

A Deep Learning System for Predicting Size and Fit in Fashion E-Commerce

3 code implementations23 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.

Collaborative Filtering Entity Embeddings

A Bandit Framework for Optimal Selection of Reinforcement Learning Agents

no code implementations10 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.

Inductive Bias reinforcement-learning +1

Select-and-Sample for Spike-and-Slab Sparse Coding

no code implementations NeurIPS 2016 Abdul-Saboor Sheikh, Jörg Lücke

As example model we use spike-and-slab sparse coding for V1 processing, and combine latent subspace selection with Gibbs sampling (select-and-sample).


Neural Simpletrons - Minimalistic Directed Generative Networks for Learning with Few Labels

no code implementations28 Jun 2015 Dennis Forster, Abdul-Saboor Sheikh, Jörg Lücke

This results in powerful though very complex models that are hard to train and that demand additional labels for optimal parameter tuning, which are often not given when labeled data is very sparse.

A Truncated EM Approach for Spike-and-Slab Sparse Coding

no code implementations15 Nov 2012 Abdul-Saboor Sheikh, Jacquelyn A. Shelton, Jörg Lücke

We investigate two approaches to optimize the parameters of spike-and-slab sparse coding: a novel truncated EM approach and, for comparison, an approach based on standard factored variational distributions.

Image Denoising

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