Search Results for author:
Found 9 papers, 2 papers with code
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.
A Hierarchical Bayesian Model for Size Recommendation in Fashion
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
The library widens the scope of dictionary learning approaches beyond implementations of standard approaches such as ICA, NMF or standard L1 sparse coding.
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.
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.
Stochastic Maximum Likelihood Optimization via Hypernetworks
This work explores maximum likelihood optimization of neural networks through hypernetworks.
Select-and-Sample for Spike-and-Slab Sparse Coding
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
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
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.
