no code implementations • 15 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.
no code implementations • 28 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.
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).
no code implementations • 4 Dec 2017 • Abdul-Saboor Sheikh, Kashif Rasul, Andreas Merentitis, Urs Bergmann
This work explores maximum likelihood optimization of neural networks through hypernetworks.
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.
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.
no code implementations • 1 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.
no code implementations • 2 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.
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.