Search Results for author:
Found 9 papers, 3 papers with code
Deep Stereo Video Inpainting
Stereo video inpainting aims to fill the missing regions on the left and right views of the stereo video with plausible content simultaneously.
Semi-Supervised Video Inpainting with Cycle Consistency Constraints
Specifically, in this work, we propose an end-to-end trainable framework consisting of completion network and mask prediction network, which are designed to generate corrupted contents of the current frame using the known mask and decide the regions to be filled of the next frame, respectively.
A Proposal-Based Paradigm for Self-Supervised Sound Source Localization in Videos
Humans can easily recognize where and how the sound is produced via watching a scene and listening to corresponding audio cues.
Description-based Label Attention Classifier for Explainable ICD-9 Classification
ICD-9 coding is a relevant clinical billing task, where unstructured texts with information about a patient's diagnosis and treatments are annotated with multiple ICD-9 codes.
Categorical EHR Imputation with Generative Adversarial Nets
Recently, researchers have attempted to apply GANs to missing data generation and imputation for EHR data: a major challenge here is the categorical nature of the data.
Uncertainty-Aware Time-to-Event Prediction using Deep Kernel Accelerated Failure Time Models
Recurrent neural network based solutions are increasingly being used in the analysis of longitudinal Electronic Health Record data.
Quantifying Predictive Uncertainty in Medical Image Analysis with Deep Kernel Learning
We propose an uncertainty-aware deep kernel learning model which permits the estimation of the uncertainty in the prediction by a pipeline of a Convolutional Neural Network and a sparse Gaussian Process.
Introspective Learning by Distilling Knowledge from Online Self-explanation
Motivated by the conclusion, we propose an implementation of introspective learning by distilling knowledge from online self-explanations.
Learning Individualized Treatment Rules with Estimated Translated Inverse Propensity Score
Randomized controlled trials typically analyze the effectiveness of treatments with the goal of making treatment recommendations for patient subgroups.
