activity
20172022
most citedTensor-Train Recurrent Neural Networks for Video Classification

55 citations · 72 across the 6 of their papers we have counts for

collaborators

7 papers

cs.LG202111 cited

Categorical EHR Imputation with Generative Adversarial Nets

Yinchong Yang, Zhiliang Wu, Volker Tresp +1

Electronic Health Records often suffer from missing data, which poses a major problem in clinical practice and clinical studies. A novel approach for dealing with missing data are…

cs.LG20213 cited

Uncertainty-Aware Time-to-Event Prediction using Deep Kernel Accelerated Failure Time Models

Zhiliang Wu, Yinchong Yang, Peter A. Fasching +1

Recurrent neural network based solutions are increasingly being used in the analysis of longitudinal Electronic Health Record data. However, most works focus on prediction accuracy…

cs.LG20212 cited

Multi-output Gaussian Processes for Uncertainty-aware Recommender Systems

Yinchong Yang, Florian Buettner

Recommender systems are often designed based on a collaborative filtering approach, where user preferences are predicted by modelling interactions between users and items. Many com…

cs.LG20211 cited

Quantifying Predictive Uncertainty in Medical Image Analysis with Deep Kernel Learning

Zhiliang Wu, Yinchong Yang, Jindong Gu +1

Deep neural networks are increasingly being used for the analysis of medical images. However, most works neglect the uncertainty in the model's prediction. We propose an uncertaint…

cs.LG2020

Learning Individualized Treatment Rules with Estimated Translated Inverse Propensity Score

Zhiliang Wu, Yinchong Yang, Yunpu Ma +4

Randomized controlled trials typically analyze the effectiveness of treatments with the goal of making treatment recommendations for patient subgroups. With the advance of electron…

cs.CV2018

Understanding Individual Decisions of CNNs via Contrastive Backpropagation

Jindong Gu, Yinchong Yang, Volker Tresp

A number of backpropagation-based approaches such as DeConvNets, vanilla Gradient Visualization and Guided Backpropagation have been proposed to better understand individual decisi…