We used Tiled Convolutional Neural Networks (tiled CNNs) on 20 standard datasets to learn high-level features from the individual and compound GASF-GADF-MTF images.
Building upon domain adaptation work, we propose gimVI, a deep generative model for the integration of spatial transcriptomic data and scRNA-seq data that can be used to impute missing genes.
Multiple imputation by chained equations (MICE) is a flexible and practical approach to handling missing data.
#4 best model for Multivariate Time Series Imputation on KDD CUP Challenge 2018
In this work, we introduce a general probabilistic model that describes sparse high dimensional imaging data as being generated by a deep non-linear embedding.
This paper takes a step towards temporal reasoning in a dynamically changing video, not in the pixel space that constitutes its frames, but in a latent space that describes the non-linear dynamics of the objects in its world.
The imputeTS package specializes on univariate time series imputation.
#2 best model for Multivariate Time Series Imputation on PhysioNet Challenge 2012
In this paper, we propose multimodal knowledge base embeddings (MKBE) that use different neural encoders for this variety of observed data, and combine them with existing relational models to learn embeddings of the entities and multimodal data.
Multivariate time series data in practical applications, such as health care, geoscience, and biology, are characterized by a variety of missing values.
#4 best model for Multivariate Time Series Imputation on MuJoCo
Datasets with missing values are very common on industry applications, and they can have a negative impact on machine learning models.