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Greatest papers with code

Image Inpainting with Learnable Feature Imputation

2 Nov 2020hukkelas/DeepPrivacy

We propose (layer-wise) feature imputation of the missing input values to a convolution.

IMAGE INPAINTING IMPUTATION

Uncertainty-Gated Stochastic Sequential Model for EHR Mortality Prediction

2 Mar 2020iskandr/fancyimpute

However, once the missing values are imputed, most existing methods do not consider the fidelity or confidence of the imputed values in the modeling of downstream tasks.

IMPUTATION MORTALITY PREDICTION

Scalable Low-Rank Autoregressive Tensor Learning for Spatiotemporal Traffic Data Imputation

7 Aug 2020xinychen/awesome-latex-drawing

Recent studies based on tensor nuclear norm have demonstrated the superiority of tensor learning in imputation tasks by effectively characterizing the complex correlations/dependencies in spatiotemporal data.

IMPUTATION TRAFFIC DATA IMPUTATION

A Nonconvex Low-Rank Tensor Completion Model for Spatiotemporal Traffic Data Imputation

23 Mar 2020xinychen/awesome-latex-drawing

Sparsity and missing data problems are very common in spatiotemporal traffic data collected from various sensing systems.

IMPUTATION TRAFFIC DATA IMPUTATION

Bayesian Temporal Factorization for Multidimensional Time Series Prediction

14 Oct 2019xinychen/awesome-latex-drawing

In this paper, we propose a Bayesian temporal factorization (BTF) framework for modeling multidimensional time series -- in particular spatiotemporal data -- in the presence of missing values.

IMPUTATION TIME SERIES TIME SERIES PREDICTION

Low-Rank Autoregressive Tensor Completion for Multivariate Time Series Forecasting

18 Jun 2020xinychen/transdim

In this paper, we propose a low-rank autoregressive tensor completion (LATC) framework to model multivariate time series data.

IMPUTATION MULTIVARIATE TIME SERIES FORECASTING TIME SERIES TIME SERIES PREDICTION

A joint model of unpaired data from scRNA-seq and spatial transcriptomics for imputing missing gene expression measurements

6 May 2019YosefLab/scVI

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.

DOMAIN ADAPTATION IMPUTATION

Imaging Time-Series to Improve Classification and Imputation

1 Jun 2015cauchyturing/UCR_Time_Series_Classification_Deep_Learning_Baseline

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.

IMPUTATION TIME SERIES TIME SERIES CLASSIFICATION

Input Convex Neural Networks

ICML 2017 locuslab/icnn

We show that many existing neural network architectures can be made input-convex with a minor modification, and develop specialized optimization algorithms tailored to this setting.

IMPUTATION STRUCTURED PREDICTION