55 citations · 138 across the 13 of their papers we have counts for
14 papers
Fine-Grained Uncertainty Decomposition in Large Language Models: A Spectral Approach
Nassim Walha, Sebastian G. Gruber, Thomas Decker +4
As Large Language Models (LLMs) are increasingly integrated in diverse applications, obtaining reliable measures of their predictive uncertainty has become critically important. A…
Data-driven Verification of DNNs for Object Recognition
Clemens Otte, Yinchong Yang, Danny Benlin Oswan
The paper proposes a new testing approach for Deep Neural Networks (DNN) using gradient-free optimization to find perturbation chains that successfully falsify the tested DNN, goin…
Facilitating Change Implementation for Continuous ML-Safety Assurance
Chih-Hong Cheng, Nguyen Anh Vu Doan, Balahari Balu +9
We propose a method for deploying a safety-critical machine-learning component into continuously evolving environments where an increased degree of automation in the engineering pr…
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…
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…
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…
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…
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…
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…
Tensor-Train Recurrent Neural Networks for Video Classification
Yinchong Yang, Denis Krompass, Volker Tresp
The Recurrent Neural Networks and their variants have shown promising performances in sequence modeling tasks such as Natural Language Processing. These models, however, turn out t…
Predictive Clinical Decision Support System with RNN Encoding and Tensor Decoding
Yinchong Yang, Peter A. Fasching, Markus Wallwiener +3
With the introduction of the Electric Health Records, large amounts of digital data become available for analysis and decision support. When physicians are prescribing treatments t…
Predicting Clinical Events by Combining Static and Dynamic Information Using Recurrent Neural Networks
Cristóbal Esteban, Oliver Staeck, Yinchong Yang +1
In clinical data sets we often find static information (e.g. patient gender, blood type, etc.) combined with sequences of data that are recorded during multiple hospital visits (e.…
Predicting the Co-Evolution of Event and Knowledge Graphs
Cristóbal Esteban, Volker Tresp, Yinchong Yang +2
Embedding learning, a.k.a. representation learning, has been shown to be able to model large-scale semantic knowledge graphs. A key concept is a mapping of the knowledge graph to a…
Learning with Memory Embeddings
Volker Tresp, Cristóbal Esteban, Yinchong Yang +2
Embedding learning, a.k.a. representation learning, has been shown to be able to model large-scale semantic knowledge graphs. A key concept is a mapping of the knowledge graph to a…